DEVELOPMENT OF LAND SUITABILITY EVALUATION SYSTEM FOR COASTAL AQUACULTURE USING ARTIFICIAL NEURAL NETWORK AND GEOGRAPHICAL INFORMATION SYSTEMS

Transcription

1 DEVELOPMENT OF LAND SUITABILITY EVALUATION SYSTEM FOR COASTAL AQUACULTURE USING ARTIFICIAL NEURAL NETWORK AND GEOGRAPHICAL INFORMATION SYSTEMS Case Study: Mahakam Delta, East Kalimantan I KETUT SUTARGA GRADUATE SCHOOL BOGOR AGRICULTURAL UNIVERSITY

2 ABSTRACT I Ketut Sutarga (2005). Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan). Under the Supervision of I Wayan Astika and Antonius Bambang Wijanarto. Land suitability is the aptitude of given type of land to support a certain use. Land suitability is one of key factors for the sustainability of aquaculture system. It can be determined by using matching methods between land suitability criteria and land characteristics, such as topography, hydrography, climate, water characteristic, risk, land cover, and spatial plan aspects. Evaluation of land for aquaculture suitability can be reached using a parametric approach, which is implemented by using the distinguish land characteristics and combination of it. Parametric approach uses the numeric value, which classifies the land based on individual characteristics. The objectives of the research are to develop a land evaluation system on aquaculture suitability by using artificial neural network (ANN) and geographical information system (GIS) and to evaluate the performance of the new developed system as compared to GIS vector spatial analysis method. Four types of suitability were defined, i.e. highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N). The new developed GIS model uses the raster format and ANN classifier. The performance of GIS model was then evaluated, and compared to the vector map overlay that used GIS spatial analysis features, including suitability distribution, covered area, processing speed and validity. The multilayer feedforward of the ANN with backpropagation learning algorithm was chosen to do the classification. GIS overlaid layer in form of spatial database was then converted into training dataset, validation dataset and prediction dataset. Training and validation dataset contains a pair of input and output, while the prediction dataset contains input data only. To compare the two methods, the Mahakam Delta-East Kalimantan were selected as a case study. Ten thematic map layers of land characteristic were involved in both methods, i.e.: water salinity, dissolved oxygen, water ph, soil texture, soil drainage, distance to rivers, pollution risk, land cover type, land use plan and annual rainfall intensity. Each feature of thematic map layers were classified and scored using 1 to 4 ranges from worst to best, respectively. The performance of ANN together with GIS was reliable, it includes the accuracy on training dataset of 97%, and validation of 96%. Distributions of each suitability of both methods are located on the same location in the study area. Area covered in the study area was 188, ha, consisting of 21, ha (11.24%) of 2

3 S1, 87, ha (46.21%) of S2, 6, ha (3.69%) of S3 and 61, ha of N. Only 0.19% of total area represents the Unclassified Area and 6.20% is No Data. Unclassified areas may be caused by less representative training dataset. No Data class represents the area that was not included in the data processing by raster data model. The research also proved that ANN together with GIS raster do the classification faster than vector spatial analysis. 3

4 DEVELOPMENT OF LAND SUITABILITY EVALUATION SYSTEM FOR COASTAL AQUACULTURE USING ARTIFICIAL NEURAL NETWORK AND GEOGRAPHICAL INFORMATION SYSTEMS Case Study: Mahakam Delta - East Kalimantan I KETUT SUTARGA A Thesis submitted for the degree of Master of Science of Bogor Agricultural University MASTER OF SCIENCE IN INFORMATION TECHNOLOGY FOR NATURAL RESOURCES MANAGEMENT GRADUATE SCHOOL BOGOR AGRICULTURAL UNIVERSITY

5 Research Title : Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan) Student Name : I Ketut Sutarga Student ID : G Study Program : Master of Science in Information Technology for Natural Resources Management Approved by, Advisory Board: Dr. Ir. I Wayan Astika, M.Si Supervisor Dr. Antonius Bambang Wijanarto Co-supervisor Endorsed by, Chairman of Study Program Dean of Graduate School Dr. Ir. Tania June, M.Sc Prof. Dr. Ir. Syafrida Manuwoto, M.Sc Date: 5

6 STATEMENT I, I Ketut Sutarga, hereby stated that this thesis entitled: Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan) is the result of my own work during the period of April until September 2005 and it has not been published before. The content of the thesis has been examined by the advising committee and the external examiner. Bogor, November 2005 I Ketut Sutarga 6

7 ACKNOWLEDGEMENT I would to extend my special thanks to the following people for their contribution to this thesis. ϖ The research was completed under the supervision of Dr. Ir. I Wayan Astika, M.Si as supervisor and Dr. Antonius B. Wijanarto as co-supervisor who have kept me on track with their guidance, technical comments and constructive criticism through all months of my research. ϖ ϖ ϖ ϖ ϖ To my external examiner Dr. Ir. Djoko Purwanto, DEA, for his corrections, suggestions and positive ideas of this thesis. To the MIT-SEAMEO BIOTROP management and staffs, as well as IPB Post Graduate who have supported our administration, technical aspects and the facilities. To the lecturers of MIT who taught me with important knowledge for my future prospects. To Bakosurtanal, especially for chairman and the head of the Center for Marine Natural Resource Survey for giving me the opportunity and funding to study for higher education. To my friends of MIT 2003 batch, I really appreciate our togetherness, solidarities, and support to finish my study. Finally I am really thank to my beloved wife Suratmi and my daughter Saras and my son Sena for your eternal love, support and patience. I dedicated this thesis to my office Bakosurtanal. CURRICULUM VITAE Ketut Sutarga was born in Banjar Satria, Jembrana, Bali, Indonesia, at August 18, He received his 7

8 Engineer Diploma in Geodetic Engineering from the Faculty of Engineering, Gadjah Mada University, Yogyakarta in From 1989 to 1993, he worked for private engineering company in the field of surveying, mapping and consulting engineering. Since 1994 to present, he has been working for National Coordinating Agency for Surveys and Mapping (Bakosurtanal), Cibinong, West Java. In 2003, I Ketut Sutarga received a financial support from the Center for Marine Natural Resource Surveys Bakosurtanal to pursue his graduate study. He received his Master of Science in Information Technology for Natural Resources Management from Bogor Agricultural University IPB in His thesis entitled Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan). 8

9 TABLE OF CONTENTS STATEMENT... I ACKNOWLEDGEMENT... ii CURRICULUM VITAE... iii ABSTRACT...iv TABLE OF CONTENTS...v LIST OF TABLES...vi LIST OF FIGURES...vii LIST OF APPENDICES... viii I. INTRODUCTION Background Objectives Problem Statement Research Output...5 II. LITERATURE REVIEW Geographical Information Systems Spatial and Attribute Data Spatial Analysis Vector Data Analysis Raster Data Analysis Artificial Neural Network Artificial Neural Network Basics Multi Layer Feedforward-Backpropagation Backpropagation Model Backpropagation Learning Algorithm Land Suitability Coastal-Aquaculture Aquaculture System Cultivable Species Previous Related Research 23 III. RESEARCH METHODOLOGY Site Descriptions Time and Location Data and Equipment 26 9

10 Data Acquisition Field Data Processing Equipments Procedures Spatial Database Preparation Mapping Suitability by Vector Analysis Map Conversion and Combination Building Artificial Neural Network Training Process Validation Map the Raster GIS and ANN Suitability Land Suitability Comparison 40 IV. RESULT AND DISCUSSION Performances of ANN and GIS Method Training Data Sets Validation Data Sets Land Suitability Map of Raster GIS and ANN Land Suitability Analysis using Vector-Map Overlay Comparison of Field Data and Land Suitability Comparison of The Two Methods Suitability Distributions Area Covers.. 52 V. CONCLUSIONS Conclusions Recommendations. 57 REFERENCES.. 58 APPENDICES

11 LIST OF TABLES No. Caption Page 1.1. Land cover change of Mahakam delta: Map and data that used in the research Classification scheme on aquaculture suitability, especially for shrimps and crabs Range of suitability in aggregate of vector spatial analysis Example of input (X) and output (Y) pattern of S1, S2, S3, and N Accuracy of ANN for training dataset processing Accuracy of ANN for validation dataset processing The Unclassified numbers of the prediction dataset Comparison between GIS raster together with ANN and GIS vector map overlay 54 11

12 LIST OF FIGURES No. Caption Page 2.1. Overlay of map layer to generate the aggregate new output The simplified configuration of an organic neuron (a), and artificial neural network (b) The sigmoid activation function Backpropagation neural network The map of study area Distribution of water characteristic point observation, from field survey (2005) and obtained from Total Indonesia ( ) Process of GIS map overlay steps of vector spatial analysis Structure of multilayer feedforward backpropagation ANN Process of GIS raster with training and validation of ANN Predicting the land suitability of GIS raster with ANN The relationship of accuracy achieved and iteration epoch number Land suitability map using raster GIS and ANN analysis Land suitability map using vector map overlay analysis Distribution of pond productivity that produce shrimp and crab in kg/ha/year Distribution of the suitability differences between GIS vector spatial analysis and GIS raster with Artificial Neural Network Comparison of area resulted (in ha) of both methods

13 LIST OF APPENDICES No. Caption Page 1. The training dataset The validation dataset The ANN prediction of suitability classes 4. List of points of water characteristic observation List of shrimp and crab yield in the study area Thematic layer: water salinity map Thematic layer: water dissolved oxygen map Thematic layer: Water ph map Thematic layer: soil map Thematic layer: rainfall intensity map Thematic layer: land cover map Thematic layer: land use plan

14 TABLE OF CONTENTS STATEMENT... i ACKNOWLEDGEMENT... ii CURRICULUM VITAE... iii ABSTRACT...iv TABLE OF CONTENTS...v LIST OF TABLES...vi LIST OF FIGURES...vii LIST OF APPENDICES... viii I. INTRODUCTION Background Objectives Problem Statement Research Output...5 II. LITERATURE REVIEW Geographical Information Systems Spatial and Attribute Data Spatial Analysis Vector Data Analysis Raster Data Analysis Artificial Neural Network Artificial Neural Network Basics Multi Layer Feedforward-Backpropagation Backpropagation Model Backpropagation Learning Algorithm Land Suitability Coastal-Aquaculture Aquaculture System Cultivable Species Previous Related Research...23 III. RESEARCH METHODOLOGY Site Descriptions Time and Location Data and Equipment Data Acquisition Field Data Processing Equipments Procedures Spatial Database Preparation Mapping Suitability by Vector Analysis

15 Map Conversion and Combination Building Artificial Neural Network Training Process Validation Map the Raster GIS and ANN Suitability Land Suitability Comparison...40 IV. RESULT AND DISCUSSION Performances of ANN and GIS Method Training Data Sets Validation Land Suitability Map of Raster GIS and ANN Land Suitability Analysis using Vector-Map Overlay Comparison of Field Data and Land Suitability Comparison of The Two Methods Suitability Distributions Area Covers...52 V. CONCLUSIONS Conclusions Recommendations...56 REFERENCES...57 APPENDICES

16 LIST OF TABLES No. Caption Page 1.1. Land cover change of Mahakam delta: Map and data that used in the research Classification scheme on aquaculture suitability, especially for shrimps and crabs Range of suitability in aggregate of vector spatial analysis Example of input (X) and output (Y) pattern of S1, S2, S3, and N Accuracy of ANN for training dataset processing Accuracy of ANN for validation dataset processing The Unclassified numbers of the prediction dataset Comparison between GIS raster together with ANN and GIS vector map overlay 54 16

17 LIST OF FIGURES No. Page Caption 2.1. Overlay of map layer to generate the aggregate new output The simplified configuration of an organic neuron (a), and artificial neural network (b) The sigmoid activation function Backpropagation neural network The map of study area Distribution of water characteristic point observation, from field survey (2005) and obtained from Total Indonesia ( ) Process of GIS map overlay steps of vector spatial analysis Structure of multilayer feedforward backpropagation ANN Process of GIS raster with training and validation of ANN Predicting the land suitability of GIS raster with ANN The relationship of accuracy achieved and iteration epoch number Land suitability map using raster GIS and ANN analysis Land suitability map using vector map overlay analysis Distribution of pond productivity that produce shrimp and crab in kg/ha/year Distribution of the suitability differences between GIS vector spatial analysis and GIS raster with Artificial Neural Network Comparison of area resulted (in hectares) of both methods 52 17

18 LIST OF APPENDICES No. Page Caption 1. The training dataset The validation dataset The ANN prediction of suitability classes List of points of water characteristic observation List of shrimp and crab yield in the study area Thematic layer: water salinity map Thematic layer: water dissolved oxygen map Thematic layer: Water ph map Thematic layer: soil map Thematic layer: rainfall intensity map Thematic layer: land cover map Thematic layer: land use plan

19 I. INTRODUCTION 1.1. Background Land evaluation is a process of assessing the land for defining the suitability for specific uses. Generally, land evaluation deals with agriculture, which provides information and recommendations for deciding 'which crops to grow where' and other related questions. The land evaluation in non-agriculture purposes has also been conducted in many applications, such as site selection for specific land allocation for waste disposal, real estate, sport center and others. The main product of land evaluation investigations is a land classification that indicates the suitability of various kinds of land for specific land uses, and it is usually depicted on maps with accompanying reports. Land suitability is the selection of suitable land, and suitable cropping and management alternatives that are physically and financially practicable and economically viable. The land suitability is the aptitude of a given type of land to support a defined use. The process of land suitability classification is the appraisal and grouping of specific areas of land in terms of their suitability for a defined use. Land suitability for certain purposes is needed to be defined to get optimal utilization of land. Government should arrange and specify the land use to have sustainability of the land and to maintain the utilization. Another important aspect of land suitability is to allocate the land availability to be used for providing an economic benefit, such as for agricultural or non- agricultural business. Investors who are interested to this land suitability will invest his/her money to gain 19

20 benefit and positive impact to increase local government revenue and local economy. Several methods have been used to define land suitability both for agriculture or non- agriculture, such as the conventional method of spatial analysis, multi criteria decision-making, and analytical hierarchy process. This research will attempt to apply the artificial neural network (ANN), that will be integrated to geographical information systems (GIS). Mahakam Delta of East Kalimantan (Indonesia) area will be evaluated for its land suitability based on physical aspects only using this method. Land suitability of that area will be defined for coastal aquaculture. Land suitability is assessed and classified with respect to specified kinds of uses. Evaluation is done through land suitability involving comparison of two or more alternative kinds of use. The suitability classes are defined by physical criteria, so that the multidisciplinary approach is required. Suitability refers to land use on a sustained basis. An ideal system to evaluate the land suitability should take into account the physical, economic, social and political context of the area concerned. However, due to the limitation of Mahakam Delta data availability, time and cost, the evaluation will be conducted based on physical aspects only. Spatial analysis represents the methods to do land suitability, which utilizes the GIS functionalities. It is conducted by superimposing one map layer to another to perform the land unit for scoring, summing, classifying and mapping the suitability classes. The process takes long time to achieve the land suitability class map, because it conducts the step by step of map overlay. 20

21 To overcome the conventional GIS-spatial analysis limitations, the automatic spatial analysis is then introduced by integrating GIS technology with neural network. Neural network represents the new method, based on simplified models of the human-central nervous system (Peterson, 1996). This method has been applied in many fields such as: constraint satisfaction, forecasting, general mapping, control, data compression, diagnostics, optimization, pattern recognition, risk assessment, and multi sensor data fusion. In this research, neural network will be applied to classify land suitability based on GIS-spatial analysis data. This research aims at defining the land suitability for aquaculture in Mahakam Delta-East Kalimantan, Indonesia by evaluating the neural network method on GIS raster data and by comparing the results against vector map overlay. The developments of Mahakam Delta are very dynamic that can be indicated on the series of land cover area recorded by the PT. Total Indonesia company (Total Indonesia, 2001) as shown in Table 1.1. The table describes the trend of land cover change from The most rapid change area is Ponds (fish/shrimp ponds) which is 0 km square in 1980 into km squares in Other significant land cover changes are: degraded forest, deltaic culture, dense avicenia, nypa and dispersed avicenia, and pure nypa. This indicates that there is a significant land utilization change from Non-Ponds into Ponds. Due to this uncontrolled land cover change trend of the Mahakam Delta, special attention is needed. Local government should facilitate the land use availabilities by rearranging and specifying it. Land evaluation should be carried out to achieve the sustainable utilization of land use. Conducting the land suitability for coastal aquaculture should solve the 21

22 trend of changing from non-ponds into ponds problems in this study area. Table 1.1. Land cover changes of Mahakam Delta No. Land Cover Area (km 2 ) Cultivated plain Degraded forest Degraded marsh Deltaic culture Dense Avicenia Dense Rhizophora Fresh-water mangrove Hilly area Mixed fresh-water forest Non-classified Nypa and dispersed Avicenia Nypa and Rhizophora Pure Nypa Sea Sonneratia Ponds Tidal flat Yard Source: Mahakam Delta landcover map series, 2001 by PT. Total Indonesia 1.2. Objectives The objectives of this research are: 1) To develop a land evaluation system on coastal aquaculture suitability by using artificial neural network (ANN) and geographical information system (GIS). 2) To evaluate the performance of the new developed system as compared to the GIS-vector spatial analysis method. The comparison covers its suitability distribution, covered area, 22

23 processing speed and validity by taking coastal aquaculture suitability at Mahakam Delta-East as a case study Problem Statement As mention earlier, land evaluation is used to define the land suitability for specific purpose such as coastal aquaculture in Mahakam Delta, East Kalimantan. This suitability classes are needed for arranging and specifying within this under developing area because of the uncontrolled land use change. This area represents the most dynamic area where the land is increasing in significant amount caused by eroded process in upper land and heaped in the coastal. So, this is why the land uses for spatial planning is needed to be emphasized in more detail. ANN is commonly used in image processing for supervised classification method of remotely sensed data. In this case, a research will be conducted on the integrated ANN and GIS based on GIS data for defining the land suitability for coastal aquaculture. The land suitability that will be considered is based only on the physical aspects. Another aspect that need to be emphasized in this research is to develop land evaluation system in the form of application software based on ANN and GIS 1.4. Research Output The final output of the research is the ANN model together with GIS to produce the land suitability map for coastal aquaculture, especially for shrimp and crab culture of Mahakam Delta East Kalimantan. The map produced by applying the GIS raster, which join 23

24 with ANN classifier. A raster spatial analysis feature in GIS is implemented for overlaying thematic map layer into new merging map, while land suitability will be classified by using ANN. On the other hand, vector spatial analysis in that study area will also be conducted. This will provide good comparison on the performances of the GIS map overlay against the proposed new method of ANN to produce land suitability map for aquaculture. The expected benefit of this research is that using the ANN method, the land suitability map of coastal aquaculture can be done more effectively and efficiently. This is not only for the case of Mahakam Delta, but also for land suitability analysis in general. In the case of Mahakam Delta, Coastal aquaculture suitability, will be assessed only for the brackish water farming in the land, such as tiger shrimp (Penaeus monodon) ponds even mud crab (Scylla serrata). The land suitability on aquaculture availability would facilitate the local government to make a decision on Mahakam Delta management. In the long term, it will be expected the method will provide good assistance in managing the area sustainably. 24

25 II. LITERATURE REVIEW 2.1. Geographical Information Systems Spatial and Attribute Data A geographical information system (GIS) is a computer-based system for managing spatial data, including the functionalities for data capture, input, manipulation, transformation, visualization, combination, query, analysis, modeling and output. (Bonham-Carter, 1994). GIS functionalities are the reliable tool for spatial analysis dealing with geo-referenced data. Geographic data are characterized by two fundamental components: the phenomenon such attribute and the spatial location of it. Geographic data are inherent form of spatial data. Geo-referenced data mean spatial data that pertain to location on the earth s surface. Spatial data are associated with map, whereas attributes data are supported with tables. The representation of spatial data is crucial for any further processing and understanding of data. In general, spatial data model consists of raster and vector models. Raster data model is a regularly spaced set of cells with associated field values. The location of geographic objects is defined by its row and column position of the cell they occupy. The associated value with each cells are not illustrated, they are individual cells. Each cell is stored in a unique value and represents an area of the land surface. 25

26 Vector data model represents the geographic objects in the real world using the points and lines that define their boundaries. Their locations on earth s surface are referenced to map position using Cartesian coordinate system. Points, lines and polygons are used to represent irregularly distributed geographic object. The spatial entities in vector model of correspond more or less to the spatial entities that they represent in the real world. An advantage of vector data model is efficient representation of topology. However, it s a complex data structure, difficult to do overlay and inefficient for image processing. On the other hand, raster data model can make simple data structure, simple to do overlays and efficient for image processing, although raster format is less compact data structure and difficult to represent topology. Raster data format enables to conduct map algebra such as for map combination. Another advantage of raster data model is that raster provides wider variety of range of attribute data, because each cells has value it self. Based on this consideration, raster data model is chosen in this research incorporated with ANN. One important thing of GIS software is spatial data organization. A spatial data layer is either a representation of continuous or discrete field, or a collection of objects of the same kind. A data layer contains spatial data of any attribute (or thematic) data. Data layers can be overlaid with each other in GIS software. Spatial data layers can perform spatial analysis such as overlay function, map algebra or other GIS analysis functionalities. Database management systems are computer systems for handling any kind of digital data. Database management systems are 26

27 the heart of any GIS. GIS system allows storing and manipulating the database. Some GIS software use internal DBMS to manage non-spatial data such attribute or thematic information, others provide linkages to external DBMS. Attribute is descriptive information characterizing a geographical feature (point, line and area). Fact describes an entity in a relational data model equivalent to the column in a relational table. Attribute values can be written based on primary observation or they can be derived by secondary processing and calculation. Attribute of spatial object are usually organized into lists or tables. It draws in form of computer organizational tables, two-dimensional arrays of number with rows being entities or object and columns being attributes. Attribute is also known as thematic information, which refers to other kinds of properties of geographical object, such as land use, annual rainfall, rock type or soil type. Each of polygon, which is coded by value of features relates to each attribute in each record. Numeric codes are the unique value that can be distinguished from others. The relationship of spatial and attribute data that describing the geographical information could be formed both in vector and raster data model. This geographic information format is simply depicted in the GIS software Spatial Analysis The principal objective of spatial analysis is to transform and combine data from diverse source into useful information, to improve one s understanding or to satisfy the requirements or objectives of decision makers (de By, 2000). Spatial analysis is the core functionalities of GIS where we could define the land capability; land 27

28 suitability for certain purpose, modeling and many other applications. Spatial analysis, in this case, will be conducted both in vector and raster data format Vector Data Analysis Spatial analysis of vector data is conducted by utilizing the GIS functionality in map overlay. Many functionalities of map overlay, i.e. union, intersect, identity, erase. Map overlay functions combine the geometry and attributes of two polygon feature maps to create a new output map. Most of map overlay will be used by applying Union operation. The illustration of Union operation is depicted in Figure 2.1. The output map represents the aggregate of the input both in spatial and attributes. Map overlay of several feature maps (layers) is destined only for polygon features. Input Layers Output Layer Layer -1 Layer Layer -n Figure 2.1. Overlay-map layer to generate the new aggregate output map For map layer consisting of line features such as rivers and pipe gas line, analysis can be done by applying a zoning of it. This can be done by applying Buffer functionality. Buffering is the creation of polygon features as far as a distance from the line center. 28

29 The most important thing in map overlay is the attribute of each layer database. Attribute of each map layer contains thematic information and its score, respectively. Attribute distinguishes one feature to others, while score determines the numeric value of attributes. Both of attribute and score of each layer are brought to the output map layer. The output map layer represents the aggregate of all input layer. Analysis of new features of output map corresponds to the total score of numeric value. Classification of total score represents feature class of output map. In this research, classification of total score aggregate is conducted by implementing the equal interval of the maximum and the minimum range of values Raster Data Analysis As mention before, the raster data model uses a regular grid to cover the space in each grid cell to correspond to the characteristic of a spatial phenomenon at the cell location. Raster data analysis can be performed at the individual cell, or group of cell. An important consideration in raster data analysis is the type of cell value and cell size. Cell size deals with the detail level of information to be created. Smaller cell size indicates more detail information and vice versa. Raster analysis begins with the set up of an analysis environment including the area extent and cell size. The extents zone of raster grid data can be performed by using mask grid functionality. Mask grid is a grid that limits raster data analysis to cells that do not carry the cell value of No Data. Typically, the output cell size is set to be equal to, or larger than, the largest cell size among the input grids. 29

30 Joining raster layer is implemented using cell-by-cell operations (or local operation). A local operation creates a new grid from either a single input grid or multiple input grids, and the cell values of the new grid are computed by a function relating the input to the output. Joining process in raster grid data looks like Union operation in vector format. However, joining process is simpler and faster rather than union operation. Analysis of raster data is incorporated with ANN Artificial Neural Network Artificial Neural Network Basics Artificial Neural Network represents the computational through parallel-distributed information structure consisting of a set of adaptive processing (computational) elements and a set of unidirectional data connections (Abrahat and Fischer (2000). These data connection (or network) are neural in the sense that they have been inspired by neuroscience as biological or cognitive neural phenomena. In fact, ANN is more common with traditional mathematical and/or statistical model, such as non-parametric pattern classifier, statistical regression model and clustering algorithm, rather than neurobiological models. The simplest neural network architecture is a single layer feedforward network. It is a single layer because the input patterns are processed through a single layer of neuron only. An input pattern is propagated through neural synaptic weight connections to the neuron where response is generated as the output activation. It is feedforward because signals propagate only in a forward direction, from the input nodes to the output node. The simplified configuration of organic neuron and its relation to the artificial model of neural network are described in Figure

31 S o m a x1 x2 x3 w 1 w 2 w 3 A xon S yn ap ses w n D e nd rite s xn Figure 2.2. Simplified configuration of an organic neuron (a), Artificial model of neuron (b), (Veelenturf, 1995). The basic computing element of biological system is the neuron. A neuron is a small cell that receives electrochemical stimuli from multiple sources and responds by generating electrical impulse that are transmitted to other neurons or effector s cells. Due to the amount of neurons that will be processed, the architecture of artificial neural network is fully done by a large number of nodes and connections. Each connection points from one node to another is associated with a weight. According to Fu (1994), the artificial neural network should involve the following tasks: determination of network properties, node properties and learning algorithm. The network properties include connectivity (topology), type of connections, the order of connections, and the weight range. The topology of a neural network refers to its framework as well as its interconnection scheme (Figure 2.4). The framework is often specified 31

32 by the number of layers and the number of nodes per layer. Three types of layers include (Fu, 1994): The input layer: The nodes, which encode the instance presented to the network for processing. For example, each input unit may be designated by an attribute value possessed by the instance. The hidden layer: The nodes, which are not directly observable and hence hidden. They provide nonlinearities for the network. The output layer: The nodes which encode possible concept (or value) to be assigned to the instance under consideration. For example, each input unit represents a class of object. Node properties of a neural network is the transfer function to be done corresponding to the activation level. The activation levels of nodes can be discrete (e.g, 0 and 1) or continuous across a range (e.g., [0,1]) or unrestricted. This depends on the activation (transfer) function chosen. If it is hard limiting function, then the activation levels are 0 (or 1) and 1. For sigmoid function, the activation levels are limited to a continuous range of real [0,1]. The sigmoid function F (Fu, 1994) is formulated in equation 1, while Figure 2.3 illustrated the bonded value of that function. 1 y ( x ) = 1 + e.1) O utput 1.0 x Input Figure 2.3. The sigmoid activation function (Fu, 1994) 32

33 The learning rule is one of the most important attributes to specify for a neural network (Fu, 1994). The learning rule determines how to adapt connection weights order to optimize the network performance. It indicates how to calculate the weight adjustment during each training cycle. The weight initialization scheme is specific to the particular neural network model chosen. In many case, initial weights are just randomized to small real numbers. The basic performance of ANN is to learn the relation between input and output, then to accommodate for getting consistent responses. Efficient learning algorithms should be developed, particularly for the network with multiple layers and large number of interconnection. Computation is done by iterative learning procedures to obtain an adequate weight value. Learning method is suited to the ANN model and mathematical model. Methods of learning can be categorized into supervised, reinforced and unsupervised. This research chooses a supervised method that applies the backpropagation algorithm for learning process Multi Layer Feedforward-Backpropagation Backpropagation Model Backpropagation is a learning algorithm using multilayer feedforward network with a different transfer function in artificial neuron. Backpropagation learning algorithm is usually implemented in multi (three or more) layer neural network. This learning algorithm accommodates both real and integer numbers for input and output. The general multilayer feedforward network is fully interconnected hierarchy consisting of an input layer, one or more hidden layer and 33

34 output layer. The hidden layer only receive internal inputs (inputs from other processing units) and hidden from outside world. The backpropagation arises from the method in which corrections are made to the weights (Patterson, 1996). During the learning phase, input patterns are presented to the network in some sequences. Each training pattern is propagated forward layer by layer until an output pattern is computed. The computed output is then compared to a desired value or target output and error value is determined. The errors are used as inputs to feedback connections from which adjustments are made to the synaptic weights layer by layer in back direction. The backward linkages are used for the learning phase, whereas the forward connections are used for both the learning and the operational phase. The processes inside the feedforward backpropagation algorithm network are described in the Figure 2.4. IN P U T L AYER x0 x1 xi w ij H IDDEN L AYER h0 h1 h2 h3 hj vjk O U TP U T L AYER y1 yk x i : input variable of node i in input layer hj : output of node j in hidden layer yk : output of node k in output layer (predicted value of node k) wij : weights connecting node i in input layer and node j in hidden layer vjk : weights coonecting node j in hidden layer and node k in output layer. Figure 2.4. Backpropagation neural network (Fu, 1994) Backpropagation Learning Algorithm 34

35 In the backpropagation learning algorithm the training instance set for the network must be presented many times in order for the interconnection weight between the neurons to settle into a state for correct classification of input pattern. The basic learning algorithm of back propagation modifies the interconnection weight on the network so that signal error is minimum (closer to zero). Figure 2.4 shows a backpropagation neural network with one hidden layer and full interconnection. Refers to Patterson (1996), the notations of network parameters are: v ij : Weight connections between input layer unit i and hidden layer unit j i = 1,2,,N, j = 1,2,,H w jk : Weight connections between hidden layer unit j and output layer unit k j = 1,2,,H, k = 1,2,,M p x : Input training pattern, p = 1,2, P p y j : Output hidden layer unit j for input pattern unit p x p z k : Output from unit k of the output layer for input pattern p x p t k : Desire or target output In each hidden layer, the net input that represent the sum of input nodes times weight can be computed as: H = Σ w x and j i ij i I = Σv h, where H j is net input of input layer-hidden layer unit j, k j jk j and I is the net input to unit k of the output layer, respectively. k Backpropagation learning algorithm can be done step by step as 1. Initialization: follows (Patterson, 1996): 35

36 a. Normalization of input data x i and target t k in form of (0, 1) range b. Randomize of weight wij and vjk using ( 1, 1) value. c. Initialize of thresholding unit activation, x 0 =1 and h 0 =1. 2. Activate of input layer-hidden layer units with: hj = 1 1 Σw ij x + e i... 2) 3. Activate of hidden layer-output layer units with: y k = e Σv jk h j. 3) 4. To minimize error of weight, v jk must be adjusted. This process is called backward step. Adjustment of v jk is done by computing error of the nodes in output layer, denotes δ k then adjusts weight v jk :. δ = 1 y )( t y )... 4) k jk ( k k k v = v + β.δ. h... 5) jk k j where: β is constant of momentum, t k is prediction value. 5. Similar to step 4, the nodes can be backward stepped in hidden layer to adjust w ij : τ k = h ( 1 h ) Σδ. v... 6) j j k k jk w = w + β.τ. x... 7) ij ij k i 36

37 6. Update all weight. Refining weight is needed when output has significant difference from input. To minimize error, each layer is refined using delta rule (Patterson, 1996): w = α.δ. h... 8) ij jk i j j v =α.δ. x... 9) k w new ij old ij where α is learning speed constant So, the refinement weight: = w + w... 10) ij v new jk old jk = v + v... 11) jk 7. Return to step 2 and repeat each pattern p until the total error has reached an acceptable level (iterative). Iteration process is done to achieve the minimum error using the refinement weight. The performance of neural network can be evaluated based on the value of root mean square error (RMSE), using: RMSError = Σ ( hk t n.. 12) The trained neural network can be used to predict target (T) by k 2 ) inputting values from input layer (X) Land Suitability Land evaluation is the process of assessing of land performance when (the land is) used for specified purposes (FAO, 1985). The land is the ultimate source of wealth and the foundation on which civilization is constructed. Due to the benefit of the land, then are merged efforts to utilize it. Land evaluation leads to rational land use planning and 37

38 appropriate and sustainable use of natural and human resources. Land suitability represents a method of land evaluation. Land suitability analysis estimates which areas are suitable or not suitable for certain development. The land suitability can be determined by using matching methods between land suitability criteria and land characteristics. The process of land suitability classification is the appraisal and grouping of specific areas of land in terms of their suitability for a defined use. The suitability is the aptitude of a given type of land to support a defined use. To produce the land suitability, two concept of land evaluation are known, i.e. physiographic approach and parametric approach. Physiographic approach utilizes landform framework to identify the natural land unit, while parametric approach divides the land following the distinguish land value and its combination. Parametric approach is more suitable for this research, due to all of parameters that are quantized. In this study two categories are recognized: orders and classes. The orders indicate whether or not given types of land are suitable for the concerned land utilizations type and are expressed by the symbols S and N (FAO, 1976): S (suitable) : Land on which sustained use is expected to yield benefits which justify the inputs, without unacceptable risk of damage to land resources N (not suitable) : Land whose qualities appear to preclude sustainability for the considered land use Classes reflect degrees of suitability within the order suitable. Normally three classes are recognized: 38

39 S1 (highly suitable) S2 (moderately suitable) S3 (marginally suitable) : Land which has no significant or only minor limitations to the sustained application of the given land utilization : Land which has limitations that are moderately severe for sustained application of the given land utilization. The limitations will reduce productivity or benefits and will increase the required inputs : Land which has severe limitations for sustained application of the land utilization Because it is based only on physical aspect of suitability orders, there will be no differentiation between N1 and N2. In this case, the not suitability of land for coastal aquaculture will be assumed as N (not suitable). N (not suitable) : The limitations are so severe that they preclude the successful application of the given land utilization 2.4. Coastal-Aquaculture Aquaculture System Aquaculture is the farming of aquatic organisms, including fish, molluscs, crustaceans and aquatic plants. The objectives of farming of aquatic organism are to have control over their growth and propagation or breeding by judicious rearing of these organisms. Rearing is intended either increasing the quantity or improving the quality of product, and make the process economically so that the farmers get some profit. Considering of coastal zone, the aquaculture system will emphasize on intertidal farming characteristic only. The specific assessments will intent for brackish water aquaculture which culturing the selected shellfish such as tiger shrimp (Penaeus monodon and crab 39

40 (Scylla serrata). Shellfish is not true fish, it includes: crustaceans class (shrimps, lobster, crab) and molluscs class (oyster, clam, mussel, scallops, abalones, conch, squid). While Fin fish is true fish, includes: mullets, milk fish, yellow-tail eel, pearlspot, red sea bream, grouper, tilapia, plaice, salmon, pompano, etc (Bose, 1991) The aquaculture system is distinguished to the level of intervention applied. These three culture techniques can be applied to increase the productivity of aquaculture system, i.e.: extensive, semiintensive and intensive (Furey and Pitman, 2003). Extensive system applied with low cost involved and minimum requirement of management. The simplest form of extensive system is the natural system, where it only utilizes the natural requirement. Types of extensive system are low input, low capital and operating cost and low level of management. Another aquaculture system such as semi intensive and intensive is carried out by higher level of interventionapplied, includes: input, cost, management aspects Cultivable Species Based on the brackish water environment, these tiger shrimps and crabs species survive with wide range of temperature and salinity. The most consideration is economics value, which represents the mean commodity of fish (or shell fish) commerce. Requirement of shrimp and crab culture should involve some physical aspect such as: soil characteristics, topographical, climate, hydrology and water quality (Hardjowigeno et al, 1996). Kapetsky and Nath (1997) added and enrich those criteria with support and input aspects, risk and natural indicators. Both researchers use the similar criteria, but the second consider the environmental aspects. 40

41 Shrimps. Shrimps or prawns represent one of food source containing a high protein. Tiger shrimp (Penaeus monodon) represent the important export commodity besides the petroleum. Consumer request to mean prawn go up 11,5% per year (Ministry of Research and Technology, 2005), therefore, this commodity represents the most species being cultured in Indonesia. The body can attain the maximum size of 27 cm in length and 130 gram in weight, however, the commercial size being above 12 to 13 cm in body length and 20 gram in body weight. Culture of tiger shrimp in brackish water very much depend on the technology aquaculture system to be applied. The suitable water salinity for shrimp is 12 to 20 ppt. Crabs. The mangrove crabs (Scylla serrata), have long been an incidental product of brackish water pond culture, and sometimes even deliberately stocked in fishponds. The market of this commodity is increasing, both for domestic and export needs. This matter can not fulfill for natural arresting from brackish water only; however, it is a challenge to meet the market through farming system (Ministry of Fisheries and Marine, 2003). Commercial size of crab body is 10 cm in length. Ponds of cultivated crab should be 0.8 to 1.0 meters deep, and the suitable water salinity is ppt. Texture of pond soils is silty loam or sandy loam. The preferred tidal difference for crab live is ranging from meters. Number of species in the ponds can be monoculture, which culture one species only or poly-culture that comprises more than one species. 41

42 Poly-culture can be applied in extensive aquaculture system, which utilizes the natural feed of brackish water environment Previous Related-Researches Site selection for development of shrimp and mud crab culture have been assessed by Salam and Ross (1997), in Southwestern of Bangladesh. Their models were developed using remote sensing and GIS tool. Remote sensing data was used for obtaining water body, GIS database incorporated the existing environmental layer such waters, soil, land use, water temperature, rain fall, salinity and ph. Infrastructure is also included, such as roads, market and processing plant. From those factors, they developed sub model by defining suitability classes: very suitable, moderately suitable, marginally suitable and presently not suitable. Then, multi criteria evaluation was applied to determine the weight of each parameter to produce the suitable sites for shrimp and crab farming. The use of remote sensing technique for shrimp farming site suitable selection was conducted in coastal area of Bangladesh (Islam et al, 1999). They used various types of data, including: different satellite data, thematic map, field measured and other published information. Six major works are construction of fisheries database, test of GIS suitable site shrimp farming, implementation of model, application and analysis of output model, socioeconomic assessment of the site, and analysis of expansion impact and risk. Fishery resources have been successfully analyzed in the areas using GIS modeling of suitable site selection for shrimp farming. Easson and Barr (1996) studied the integration of ANN and GIS to interpret natural resource information. The feasibility of the integration 42

43 of these two technologies has been proven in recent research and has potential applications to other forms of geological and hydrological interpretations. Land evaluation expert systems, which is an automated land evaluation system with the transformation of land suitability classification expertise, following the FAO framework, into trained ANN has been developed by Bandibas (1998). An error back-propagation ANNs model was used. The trained ANNs were stored in a database, representing the knowledge base. This knowledge base was used in a land system successfully to determine the best land suitability class in the area for corn and rice cultivation. 43

44 III. RESEARCH METHODOLOGY 3.1. Site Description Administratively, Mahakam Delta is governed by Kutai Kertanegara Regency, East Kalimantan Province - Indonesia. This area covers more than 2000 km squares. Geographically, it is located between E, and S. This area represents the downstream of Mahakam Rivers while upper course is in the Center of Borneo Island, as shown in Figure 3.1. Figure 3.1. The map of study area 3.2. Time and Location Mahakam Delta The research was carried out in two separate locations. Preparation, field data computation, application of data processing and report writing were conducted in Bogor, West Java starting from March- August, Meanwhile, field data acquisition was acquired in Mahakam Delta and the Government of Kutai Kartanegara Regency - East Kalimantan, from July 24 30,

45 3.3. Data and Equipments Data Acquisition The main spatial data of Mahakam Delta to be implemented for supporting the research objectives include soil characteristics, water characteristics, climate characteristics, land use/land cover and spatial plan. Another aspects involved in determining the site suitability are rivers and risk factor. Supporting topographical and hydrological aspects, in this research are very important. However, it is difficult to distinguish the altitude on map in this area. This is because the area being studied is tidal flat, where altitudes ranges from 0 3 meters above the mean sea level. Also, it is difficult to determine which area is covered by tidal zone due to the limitation of 1:50000 map scales, where contour interval is drawn at 12.5 meter. Generally, the intertidal zone range of 2-3 meters above the mean sea level and it amplitudes meters is suitable for shrimp ponds development (Purnomo, 1992). Based on the tidal requirement, Mahakam Deltas are inclusive of area that are suitable for brackish water farming where the ranges of tidal are 2.5 meters. Most of the data for processing were secondary data, which were taken from previous study and part of them were taken from field survey. Secondary data include soil texture, soil drainage, land use, spatial plan, gas pipelines and rivers map. That map represents features being necessary to GIS processes. The distribution of field water measurements is shown in Figure 3.2. Some of the data are secondary data that were obtained from the PT. Total Indonesia company (2004, 2005a). Water parameters were 45

46 observed in the field using water quality checker instrument, including: Figure 3.2. Distribution of water characteristic point observation, from field survey (2005) and obtained from Total Indonesia ( ) Salinity. Salinity represents the total concentration of dissolved salt in the water that expressed in mg/l or ppt. Intertidal is an area that represent the mixture of fresh and saline water that perform the brackish water. Salinity of intertidal may differ between low and high tides. Previous salinity map was utilized, and the new observation is conducted to obtain the salinity on point data. Measurement was held by points on rivers, ponds or channel and offshore. 46

47 Dissolved Oxygen (DO). DO is one of water variables that is significant for aquaculture. Dissolved of oxygen in the water was influenced by other variable such as temperature, salinity, organic matter and brightness. ph. Grade of acidity represents the alkaline intensity of hydrogen ion of liquid. ph is very significant for brackish water aquaculture, so that its concentration should be well balanced to take care of cultured species Referring to the suitability to be defined, then shrimp and crab yield of pond in the field were recorded from fisherman. However, these data of ponds-harvest were taken from previous report that studies the productivity of ponds in Mahakam, it includes: shrimp, milkfish and crabs (Total Indonesia, 2005b). The particular yield of shrimp and crab in kg/ha/year of ponds will be used as comparator of the suitability map which will be produced by GIS model Field Data Processing Some of data obtained from field should be processed so that it will be ready for next GIS and ANN processing, and these include water salinity, water dissolved oxygen, water ph and rainfall intensity. Water characteristics such as salinity, ph and dissolved oxygen need to be computed and interpolated before drawn on reference map. Also, the rainfall data in this area of periods are necessary to compute for getting the annual rainfall, and then interpolate the intensity to make the rainfall intensity zone map. Interpolation of rainfall intensity was done which refers to the rainfall station distribution in Mahakam Delta area. Meanwhile, water characteristic point data that are distributed on whole area can be interpolated after drawn on reference map. These 47

48 point data represents the matured data of field that observed using water checker tool. For interpolation processes of all observed field data, the kriging features in GIS functionalities were applied to create isohyets map, including salinity, dissolved oxygen, ph of water characteristic and the rainfall intensity. The thematic maps, which were resulted from field data processing, can be shown in the Appendices 6 to 12. Both primary and secondary data sources and their edition, which will be used in the GIS raster together with ANN and GIS vector map overlay analysis are described in the Table 3.1. Table 3.1. Map and data used in the research No. Map/Data Edition Sources 1. Water Salinity, DO 2005 Field survey and ph 2. Rainfall data Agricultural Office of Kutai Kartanegara Regency 3. Topography 1991 National Coordinating Agency for Surveys and Mapping (Bakosurtanal) 4. Soil texture, drainage 2000 Center of Soils and Agro Climate Research (Puslitanak) 5. Land Cover 2001 PT. Total Indonesia 6. Pipeline Distribution 2001 PT. Total Indonesia 7. Spatial Plan (Land Use Planning) 2003 Regional Plan and Development Office of Kutai Kartanegara Regency Equipment Equipment for this research includes field survey and laboratory tools. For field survey the global positioning system (GPS) and water quality checker were used. GPS was used to determine the position on 48

49 earth s surface of sample point location to be plotted/drawn on the map. Six water parameters were observed using the water quality checker instrument, including ph, conductivity, turbidity, dissolved oxygen, temperature and salinity. The equipment for data processing in laboratory includes GIS software such as ArcView GIS 3.3 and ArcGIS 9.0. ArcGIS 9.0 support both raster and vector spatial data model. Facilities to data converting and map algebra are very adequate in this software package. Microsoft Visual Basic 6 were used to develop computer program for ANN. The hardware used to process the spatial data is PC Intel Pentium III with 256 MB of RAM. The storage device is 20 GB of hard disk and movable storage such 250 MB of flash disk. Another external storage device is CD, flash disk and floppy disk drives Procedures Spatial Database Preparation Data preparation is started from scoring each attribute of criteria. Scoring aims at providing a quantity value of each class of attribute of each layer. Each layer breaks down into certain number classes based on the attribute of data sources. All layer are broken down into four scales of score. The score is ranked by providing the weight of each criterion and combining multiple sources of evidence. The assignment of weights to maps is carried out either by analyzing the importance of evidence relatives to the experience or by using subjective judgment of related corresponding scientist. Based on the criteria from blend of scientist and the availability of the data and also considering the specific characteristic of the region to be assessed, defining the physical aspect of coastal-land aquaculture 49

50 are presented in the Table 3.2. Table 3.2 describes the requirement of suitability of aquaculture that represents the GIS database layers within its attributes and scores. Both vector and raster format will be used and applied those requirements. Spatial analysis is conducted with involving these factors using spatial analysis features in GIS. Table 3.2. Classification scheme on coastal aquaculture suitability, especially for shrimps and crabs No. Requirement/Factors A. B. C. D. E. F. G Water Parameters Dissolved oxygen (mg/l) Salinity (ppt) Water ph Infrastructure Distance to rivers (meter) Soil Parameters Soil drainage Soil texture Pollution Risk Distance to pipe line (meter) Natural Indicator Mangrove Ecosystem (Land Cover) Spatial Plan Suitability and Scores HS (4) MoS (3) MaS (2) NS (1) > Note: HS (4) = Highly Suitable, MoS (3) = Moderately Suitable, MaS (2) = Marginally Suitable, NS (1) = Not Suitable , , < , 4-6 < 1 > 50 > 11, < > 900 Very poor >75% fine Poor >75% medium, 50-75% medium Moderately poor, good 50-75% coarse, <50% all Very good >75% coarse > Rhizopora, Avicenia, Sonneratia Tambak, Nypa+Rhizopora Mangrove, Pure Nypa Tidal zone, degraded forest Spatial plan map Ponds zone Climate Rain fall (mm/year) , < 1000, > 3500

51 Source: This table was composed based on referenced of: Hardjowigeno (1996) and Kapetsky and Nath (1997), field survey, interview to petroleum company authority, fisherman ponds and interview to aquaculture expert (2005) Mapping Suitability by Vector Analysis Map overlay for vector data format is conducted to obtain an aggregate of layers that determines the suitability. It includes ten map layers with its score as described in Tables 3.2. In vector, the map overlay operation is done in pairs. For a more than two layers to be overlaid, it will be taken several steps. For example, 10-layers amount will be taken 9 steps of Union map overlays operation. Each map layers contains the database and score as prepared in Table 3.2, then overlay step by step as illustrated in Figure 3.3. The Union overlay operation will perform the new polygon features based on the origin layers. So, the final layer of Union will contain 10 layers distinguish features. The final layer of overlay is a map containing the aggregate features of each composer layers, such as attributes and score. The score are then summed to produce the total sum up of all score. The total score need to be classified into four suitability s Layer-1 Layer-2 Layer-3 Layer-4 Layer-5 Layer-6 Union- 1 Unoni- 2 Union- 3 classes: S1, S2, S3, N. Union- 4 Union- 5 Union- 6 Layer-7 Union- 7 Layer-8 Union- 8 Layer-9 Layer-10 Union- 9 51

52 Figure 3.3. Process of GIS map overlay of vector spatial analysis Suitability will be defined by implementing the parametric approach, which classified the land based on the grades or value of distinguish certain land and combines it grades to obtain the suitability (Sitorus, 1998). Parametric approach is appropriate for evaluating the land that superimposes the isohyets or isotherm map including the quantitative variable to produce the aggregate map. Classification of aggregate score is based on addition method (Sitorus, 1998) where each individual each layer will be considered as proportional weight, then classify its score according to the number of class to be assigned. In this case, the algebraic addition methods will be implemented with the same weight. Summing of the total score and classification as follows: Total_Score = Layer1_Score + Layer2_Score + Layer3_Score + Layer4_Score + Layer5_Score + Layer6_Score + Layer7_Score + Layer8_Score + Consideration of class-range division and classification involve several factors, such as number of layers, maximum and minimum layers score and total score. Based on these, the defined maximum and minimum score are 40 and 0, respectively. The most suitable of S1 should be faithfully by 40, S2 will be exactly 30 of total score, S3 should be 20 and N should be 10. However, the total score is very immeasurable due to those scores that were performed by ten parameters as map overlay result. Because of the range of the total 52

53 scores, four suitability classes can be defined by classifying them as shown in Table 3.3. Table 3.3. Ranges of class suitability in aggregate layer of vector spatial analysis No. Range of Total Score Class Suitability S S S N Source: GIS vector map overlay analysis for 10 parameters layer, which is scores by ranges from 1 to Map Conversion and Combination Those vector formats layer are then converted into raster using conversion tool in ArcGIS9.0. Conversion from vector into raster aims at providing a great variety in computing using neural network, where the data have wider range between the minimum and the maximum volumes. Next stage is to combine all raster maps that accommodate the above criteria. The conversion processing of vector data into raster format are known as rasterizing. One important thing in rasterizing is the cell size of new raster format to be formed. In this case, it will be formed into 30-meter cell size, meaning that the minimum areas unit to be assessed of new raster map is 30x30 meter squares. In rasterizing process, all thematic map criteria will be integrated into new single raster map. The new single raster map as a result contains all scores of the criteria called as map combination. 53

54 Combination process is done in ArcGIS9.0 using Combine facility. The combined map is now ready to be next processed. Map combination represents the integration model that symbolizes mathematical model, using arithmetic and logical operations to combine map layers together. The combined map can now be treated as single map, revealing the spatial relationship of each previous attribute. In this software package several maps and tables of attribute data can be combined into a single processing step Building Artificial Neural Network Training Process The training phase is the most important aspect in neural network modeling because the weights and the network characteristics is defined to be used later on other datasets. Another important thing is the normalization of the data to be in the range of 0 to 1, and then renormalize it after the testing phase. Finally, the case has to be randomized before splitting the H IDDEN LAYER h 0 data into the training, cross validation IN PU T LAYER h 1 O U TPU T LAYER and x 0 the test h 2 datasets. Salinity x 1 h 3 D O x 2 y 1 S1 ph x 3 y 2 S2 Soil texture x 4 Soil drainage x 5 h 14 y 3 S3 distance to rivers x 6 h 15 y 4 N R isk pollution h 16 x 7 Land cover x 8 h 17 Spatial plan x 9 h 18 h 19 R ainfall x 10 h 20 54

55 Figure 3.4. Structure of multilayer feedforward artificial neural network The neural network used in this study has three layers representing the input, the hidden and the output. The number of nodes in the input layer depends upon the number of corresponding neural network. In this case 10 nodes for processing element of inputs will be used to define four possible output-training patterns (i.e. land suitability classes). These four output patterns correspond to the ten input to generate the relationship in form of weight in the ANN system. Determination of the range value of each suitability classes is based the overall aggregate of total input parameters. The input pattern will define the output land suitability, then the output pattern is formed in the range of 0 and 1, respectively. The neural network process that uses multilayer feedforward backpropagation process in this research are described in Figure 3.4. The backpropagation learning is inclusive of supervise, which determines the output from the input by using the training set. Pairing the input and output of the training set in ANN were based on the suitability resulted from previous vector map overlay analysis. Selection of the training set was conducted by utilizing the known field check data and other secondary information relating to this area. Set of input (X) and output (Y) then composed in tabular form as follows Table 3.4. The table represents only for the example of training set, which 55

56 describes the relation of input and output. The table is part of the all training set. This training set will be used as input parameter to produce the weight in the ANN using MS Visual Basic 6 programming language. Number of data to be used as data training in this structure is 22% of total data set. The logistic constant is 0.2, and momentum constant (β) in this case is 0.3. Table 3.4. The example of input (X) and output (Y) pattern of S1, S2, S3, and N. N O X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 Y1 Y2 Y3 Y4 MARK S S S S S S S N S S S N S S S S S S N S2 Note: X1: Water salinity, X2 : Water DO, X3 : Water ph, X4 : Soil texture, X5 : Soil drainage, X6 : Distance to water body, X7 : Risk pollution, X8 : Land cover, X9 : Spatial plan (Land use plan) and X10 : Annual rainfall intensity Validation 56

57 Validation refers to determining whether the system can perform at an acceptable level of performance in terms of accuracy and efficiency. For classification task, performance can be defined in terms of the correct rate of classification over test cases. Validation step is conducted to test the relationship between prediction and target output. It is conducted by checking the performance by calculating the accuracy between observed and predicted values of data sets: Y Accuracy (%) = x100 % T... 15) where, Y is the number of valid prediction and T is the number of data to be predicted. Validation step will be conducted using quantitative methods, validating the performance, and computing the ratio of the number of true estimated value to the number of training data set. Number of validation dataset will be 15 % of total dataset, consisting of corresponding class suitability. This method aims at knowing the percentage of the true estimated value against the number of training set data of each suitability class S1, S2, S3, and N. Learning process that used the training data set and continuing with the validating process are described in the Figure Mapping Suitability by GIS Raster and ANN Predicted values aim at computing the datasets as if were testing. The results are then written into databases that indicate the output of the suitabilities. Datasets used for this step represent the regular input pattern beside the training sets. The ANN results are the attribute of 57

58 suitability classification in the form of database or can be worksheet containing correspond S1, S2, S3, and N. Records of prediction dataset describe this suitability classes. The result needs to be mapped to recheck the final result. W aters Infrastruc tures Soils Risks Natural CONVERSIO N TO RASTER FORMAT W -DO W -ph Spa. Plan S-Texture R-Pollution W -Salinity D-Riv ers S-Drainage Rainfall N-M angrv M AP OVERLAY M ap combination of 10 single layer Train several times using initial value TRAINING SPLITING DATASETS Randomize cases Split subsets (train and validation) in database format N o N o ACCURACY Yes SAVING THE W EIG HT VALIDATION Check performance using v alidation datasets Not satisfied, go backward Satisfied, go forward Check the accuracy No satisfied, modify iteration number, logistic & learning const Satisfied, go forward Yes Sav e the result/weight when successfully STO P Figure 3.5. Process of GIS rasterizing with training and validation of ANN Some GIS functionality will be implemented to map the result. It includes joining spatial database of raster grid map and ANN classification attribute database. Both databases in GIS can be joined if there a related item. In this case, the item relation of both is the features identity in the database record. The spatial database is marked by identity of each feature. Meanwhile the ANN database also contains the same record added with the attribute of suitability. Then, both of 58

59 them is joined and classified to produce the land suitability map for aquaculture. The scheme of the prediction process that represents the whole data input of this research can be described on the flowchart as shown in Figure 3.6. In the flow chart, the system will be started from the input of land parameters of physical aspect to produce suitability classes and then compare to the result of vector spatial analysis. S TART INPUTIN G D A TASETS N ew input data without target TH E ANN W E IG H T P R E D IC T IN G THE S U ITABILITY C A LCULATING TH E AREA C alculating the area using the cell size and number cell at each suitability level C O M PA R IS O N TO VECTOR GIS C omparation the suitability distribution, covered area, processing speed and validity FIN A L MAP Figure 3.6. Predicting the land suitability of GIS raster with ANN 3.5. Land Suitability Comparisons In order to indicate the accuracy and reliability of the land suitability produced both GIS raster together with ANN and vector map overlay, three types of analysis will be used. These are: 1. Accuracy assessment of new development methods, 2. Spatial distribution analysis of S1, S2, S3, N, respectively, of both methods, 59

60 3. Assessment of.the amenity level of both methods that correspond to the application to produce the land suitability. 60

61 IV. RESULT AND DISCUSSION 4.1. Performances of GIS Raster and ANN Method Training of ANN Data set for training and validation were composed from a combined raster multilayer database. The database contains ten single layers, which were combined using Combine functionality of ArcGIS9.0. Number of training data set recorder were 319, which were derived from raster multilayer database, consisting of 58 of S1, 222 of S2, 24 of S3 and 15 of N. The 319 training datasets of each suitability were obtained from the score of each layer, so that the level of suitability refers to as in Table 3.2. The suitability of the training dataset being used was formed into binary (as in Table 3.4). Using the logistic constant of 0.2 and momentum constant (β) of 0.3, the number of iteration obtained was 377, the result of training process can be seen in Figure Accuracy Level (%) Iteration Epoch Figure 4.1. The relationship between accuracy and iteration epoch number The result indicates that using the logistic constant and learning rate the iteration process will be stabled at 377 on which the accuracy achieve 97% as shown in Table 4.1. This accuracy value indicates that the data training set are highly 61

62 consistent. The S1, S3, and N were achieved highest accuracies due to the input pattern are highly of representative to produce the output suitability. Also, it may occur due to the number of input in training data set are suitable to the number of output. In contrast, for S3 was achieved at an accuracy of 96%. It was caused by the input pattern that was less representative to produce output or it can be the number of input of training data set that were less suitable to the number of output. However, the S3 accuracy constitutes a high accomplishment. Table 4.1. Accuracy of ANN for training dataset processing No. Output Label Number of Training Dataset Number of Valid Prediction Accuracy (%) 1. S S S N Total Validation Validation of the process was conducted using validation data set that were also resulted from combined raster multilayer database. The number validation dataset was 233, and was dedicated for testing the ANN performance. Validation was done to check and verify the consistency of system. Validation process was conducted as if the computation running the training data set. So, the number of logistics constant and learning rate were similar to the training process without iteration process. Computation using training data set was to determine the weight of the ANN. Weight value was then used as input for validation data set to produce overall accuracy. Overall accuracy represents the performance of the system that being assessed. Overall accuracy values represent the steps to test the validity whether the system run well or not. If the result were not satisfactory, for instance, it will be recalculated back to the learning step. Meanwhile, if the accuracy were good, then go to the map prediction to map the suitability (see Table 4.2). 62

63 The overall accuracy of validation dataset is 96%, and this indicates that the performance system was good. This accuracy can be accepted to the prediction of suitability class processing. As discussed in previously, lower accuracy was achieved due to less representation between the input and output pattern. Also, it may occur because the number of training dataset was less suitable. However, the overall accuracy of 95% was achieved, and this indicates that the system has met the requirement to be continued for prediction. Table 4.2. Accuracy of ANN for validation dataset processing No. Output Label Number of Training Dataset Number of Valid Prediction Accuracy (%) 1. S S S N Total Land Suitability Map of GIS Raster and ANN Based on the overall accuracy in validation datasets (as shown in Table 4.2), then prediction for map classification were conducted. Mapping the suitability classification resulting from ANN was carried out in ArcGIS 9.0 by applying the database join and mapping the legend editor. The map result are shown in Figure 4.2, where each suitability was placed on the land of the coastal area, where 21, ha (11.24 %) represent of S1, 87, ha (46.21 %) of S2, 6, ha (3.69 %) of S3 and 61, ha (32.47 %) of N. Most of study areas were covered by its suitability, 6.20 % of No Data and only 0.19 % represents unclassified area. No Data class represents the area that was not included in data processing by using raster format. Unclassified areas were produced by ANN, which uncategorized to S1, S2, S3, and N. This may occur due to really unclassified, where predicted output values of ANN in form of [0000]. Otherwise, it may occur due to the ANN output were 63

64 [1100] or [0110] or [1010]. This mean that those features of output was considered to Unclassified due to these output were belonged two or more different suitability. The number of each record was unclassified for the prediction data set are shown in Table 4.3. Table 4.3. The Unclassified numbers of the prediction data set. No. S1 S2 S3 N Total Note Unclassified Belong to S1 and S Belong to S1 and S Belong to S2 and S3 Total 15 On the map, Unclassified areas were distributed on certain location. It may occur due to lack representation of training dataset, so that this affects on the weight value and final map result. Or this can be caused by lack of number of training dataset. The composition value of suitability S1 S2 S3 N was de-normalized from number between 0 and 1. Those values represent the integer format that needed to make the classification. Actually, these values can be modified to make the fix suitability classes. However, this modification will need additional effort in MS Visual Basic 6.0 program. After conducting check and recheck on all above by modifying iteration and logistic constant, then the final overall accuracy was improve 98%. It was achieved by setting the momentum constant (β) of 0.3, logistic constant of 0.2 and iteration number of 500, on which the iteration was stabled at 377 epochs. This shows that the system performance was good, and was indicated by the number of unclassified areas, which was only 0.19 %. Geographically, the S1 are located on the back of buffer zone and placed on the ponds zone of spatial plan map. S2 were distributed outside the ponds zone and placed on the buffer zone and excludes of S1 zones. S3 areas are located on a limited 64

65 area; they are distributed along the main land coast. N is distributed on main land and parts of it were placed on No Data of study area. The detailed information of suitability is shown in Figure 4.2. Based on the literature, S1 is the best choice due to no limitation to be developed for shrimp ponds. The S1 distribution is considered appropriate for spatial plan arrangement Land Suitability Analysis from Vector Map Overlay Land suitability using vector-map overlay is fully carried out in ArcGIS 9.0 software, including scores summing, classifying and mapping. Classification of aggregated map overlay score is conducted based on Table 3.3 requirements and the suitability map as a result are shown in Figure 4.3. The areas of S1 is 18, (9.50 %), S2 is 92, ha (48.50 %), S3 is 10, ha (5.40 %), and N is 69,776.93ha (36.61.%). 65

66 Figure 4.2. Land suitability map using GIS raster analysis and ANN 66

67 Figure 4.3. The land suitability using GIS vector-map overlay analysis 67

68 The distribution of S1 were behind the green belt of coastal and rivers lines. This is caused by several layer-factor influent such as salinity, landcover and spatial plan zone. S2 were distributed along the delta and between two green belts of the delta, while S3 were located along coast of the main land. Meanwhile, N is distributed in the main land and far enough from coastline. There are no Unclassified and No Data resulted from this method. All layers that performed the aggregate union new layer were fully observed. This means that the results for this method were achieved by computation and classification process on the layer data sources Comparison of Field Data and Land Suitability Map Field data of the ponds productivity comprises of shrimps and crabs in kg/ha/year units. This data is taken from previous study conducted by PT. Total Indonesia in cooperation with Faculty of Fisheries and Marine Science Mulawarman University, Samarinda, The data were collected with no considered whether the ponds in extensive or intensive of aquaculture system. So, this ponds data were mix among extensive, semi intensive even intensive of farming system. The annual yields were presented in kg/ha/year unit. The tabular data of yield is attached in Appendix 5. The productivity are presented on map as shown in Figure 4.4. Comparing Figure 4.4. to the both previous suitability map indicates that most of the higher productivity of shrimp pond were distributed on the same location. Meaning that the S1 suitability of the model is close to the location of high productivity ponds. Exception of it were occur of two ponds, where it location excludes of S1. It s may occurred due to these two ponds were developed with fully intensive of aquaculture, which involves the management. 48

69 Figure 4.4. Distribution of pond productivity that produces shrimp, fish and crab in kg/ha/year (Source: Modified from Total Indonesia, 2005). 49