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

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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. 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. 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

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

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