|
|
|
- Henry George
- 5 years ago
- Views:
Transcription
1 " " ١* / / : / / :... ( (GIS) ). ( ). (MLP) ) ١ dianatig@modares.ac.ir : ١۵٣
2 1397 / ( ) ) ( ) ) -. SPSS.. GIS.. ( / ) / (Rsqr) GIS.. GIS GIS. MLP : :. -..( ).. Maier and ) ( ANN) (Dandy, Artificial Neural Network ١۵۴
3 ..(Gangopadhyay et al., 1999; Dayhoff, 1990). (GIS ) ( ).( ) ( )..( ) ) ().( ( ).. (Melesse, 2005)... (Krishna et al, 2008 a,b). (Zare Chahouki et al., 2012).. ( ) 3 Geographic Information System ١۵۵
4 1397 / (GIS).(Gholami et al., 2016) ( ).. ( ).( )... ( / ) ( / ) /. - / ( ).. ( ) ١۵۶
5 .. ( ). Quickbird... ( ) ١۵٧
6 1397 /...( )... ( ) (Qickbird). ( ) ). ١۵٨
7 : (DEM) ( ).. ( ) ( )... ( ) ( ). ). ( -. ( ) ). (.( ) ). ( GIS.. ( ) ١۵٩
8 1397 / SPSS.. NeuroSolutions.( )..(Nielsen, 1990; McCoord and Illingworth, 1990) MLP..( ). (FF ).( BP)..( ) : ( ) Cross Validation.. - ( ). - Momentum (Momentum GDX CG LM ) 4 Multi Layer Perceptron 5 Feed Forward 6 Back-Propagation 7Levenberg Marquart. 8Conjugate Gradient. 9Gradient Descent ١۶٠
9 .. ( ) -.. ( ) ( ).(Nielsen et al., 1990) (Rsqr) : (MSE) (MSE) ( Pi Pi) MSE = n Rsqr = [ n n i= 1 ( Pi Pi).( Pˆi P ~ i) n ( Pˆi P ~ i) (Rsqr) ] ( Pi Pi). i= 1 i= 1 Pi P ~ i Pi n Pˆi.. ).(. ). ( 10 Mean Squared Error 11 Coefficient of correlation ١۶١
10 1397 / SPSS.. ( ) ( ). ( ) Stepwise (Sig.).. (Rsqr).. (GIS) (ANN) ) ( ) ١۶٢
11 ( ). ( ). ( )..... ( ).... SPSS. ) ( ;Jafari et al., 2004).( ١۶٣
12 1397 / ( ) ( ). / (Rsqr).. Rsqr MSE. ( ) ( ). ( ). ١۶۴
13 . : ( ) CVLog= 9.51Log Soildepth Log LR LogH LogPgrowth LR Soildepth CV : - Pgrowth H (Rsqr>07,..Sig <0.05) -. / Rsqr. / MSE. - / / MSE / / Rsqr. -.(Blancoet al., 2013).. GIS. ( ).. ١۶۵
14 1397 /.(Gangopadhyay et al., 1999; Krishna et al., 2008). ١۶۶
15 ١۶٧
16 1397 / / ( ) ) / / / / / / / / / / / / / / / / / / / - / / / / - / / / ١۶٨
17 MSE MSE / / / / / / / / MSE NMSE MAE Min Abs Error Max Abs Error Rsqr / / / / / / ١۶٩
18 1397 /. ( ) ١٧٠
19 .... ).( ).(Jafari et al., 2004) ( ( )...(Gholami et al., 2015).(Aeinebeygi and Khaleghi, 2016) ;Jafari et al., 2004).( ).(.. (Barr, 2002; Hill, 2001,) ١٧١
20 1397 /.... ( ) :( ) ( ) : )....( :( ) ١٧٢
21 ....( : ). :( ).....( : ).- :......( ).- :( ) :( ) :()......( : ). :( ).... ( : ). :( )....- :() :( ) ١٧٣
22 1397 / Aeinebeygi, S., Khaleghi, M.R An Assessment of Biennial Enclosure Effects on Range Production, Condition and Trend (Case Study: Taftazan Rangeland, Shirvan). International Journal of Forest, Soil and Erosion (IJFSE), 6(2): Blanco, P., Delvalle, H.F., Bouza, P., Metternicht, G.I., Hardtke Ecological site classification of semiarid rangelands: Synergistic use of Landsat and Hyperion imagery, International Journal of Applied Earth Observation and Geoinformation,29(1): Barr, T Application of tools for hydraulic power point presentation.105- uppergotvand hydroelectric power project feasibility study Reservior Operation Flood.14p. Dayhoff, J Natural Networks archictures: Anintroduction. New Yourk: Van NostrandReinhold. Gangopadhyay, S., Gautam, T., Gupta, A Subsurface characterization using artificial neural network and GIS Journal of Computing in Civil Engineering, Co13(3): Gholami, V., Chau, K.W., Fadaee, F., Torkaman, J., Ghaffari, A Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers.journal of Hydrology,529: Hill, M Flood plain delineation using the HEC-GeoRAS Extention for Arcview. Brigham Young University, 514p. Jafari, M., Zare Chahouki, M.A., Tavili, A., Azarnivand, H., Zahedi Amiri, G.H Effective environmental factors in the distribution of vegetation types in Poshtkouh rangelands of Yazd Province (Iran). Journal of Arid Environments, 56(4): Krishna, B., Styaji Rao, Y.R., vijaya, T Modeling ground water levels in an urban coastal aquifer using artificial neural networks. Journal of Hydrological process, 22(8): Krishna, M., Neaupane, M.D., Moqbul, H Predicting Arsenic Concentration in groundwater using GIS-ANN hybrid system, 3rd IASME / WSEAS Int. Conf. on water resources, Hydraulics and Hydrolgeology (WHH '08), University of Cambridge, UK, Feb , Nielsen, H. R Neurocomputing. Addison-Wesley Publ. Co., Reading, MA. McCoord, N.M., Illingworth, W.T A practical guide to neural nets. Addison- Wesley, Publ. Co. the University of Michigan, 344p. ١٧۴
23 Maier, H., Dandy, G Neural networks for the perdictions and forecasting of water resources variables: review of modeling issues and applications. Environmental Modelling and Software, 15; Melesse, A. M., Hanley, R. S Artificial neural network application for multiecosystem carbon flux simulation. Ecological Modelling, 189, Zare Chahouki, M.A., KhalasiAhvazi, L., Azarnivand, H Comparison of three modeling approaches for predicting plant species distribution inmountainous scrub vegetation (Semnan ranglands, Iran). Polish Journal of ecology, 60 (2): ١٧۵
Keywords: Neural Network, Geostatistics, Zoning, Prediction, Groundwater Level, Jahrom, Feed- Forward, Network
INVESTIGATION OF THE SPATIAL AND TEMPORAL CHANGES IN GROUNDWATER LEVEL USING GEOSTATISTICAL METHODS(KRIGINGS) AND VALIDATION OF MODEL INPUTS BY THE USE OF ARTIFICIAL NEURAL NETWORK TECHNIQUE(MLP- RNN)
VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA USING REMOTE SENSIGN AND GIS TECHNIQUES
VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA USING REMOTE SENSIGN AND GIS TECHNIQUES A.G. Koppad a, B S Janagoudar b a Professor and Head (NRM),
Calibration of a Rainfall Runoff Model to Estimate Monthly Stream Flow in an Ungauged Catchment
Computational Water, Energy, and Environmental Engineering, 2015, 4, 57 66 Published Online October 2015 in SciRes. http://www.scirp.org/journal/cweee http://dx.doi.org/10.4236/cweee.2015.44006 Calibration
EFFECT OF ANCILLARY DATA ON THE PERFORMANCE OF LAND COVER CLASSIFICATION USING A NEURAL NETWORK MODEL. Duong Dang KHOI.
EFFECT OF ANCILLARY DATA ON THE PERFORMANCE OF LAND COVER CLASSIFICATION USING A NEURAL NETWORK MODEL Duong Dang KHOI 1 10 Feb, 2011 Presentation contents 1. Introduction 2. Methods 3. Results 4. Discussion
Real Time wave forecasting using artificial neural network with varying input parameter
82 Indian Journal of Geo-Marine SciencesINDIAN J MAR SCI VOL. 43(1), JANUARY 2014 Vol. 43(1), January 2014, pp. 82-87 Real Time wave forecasting using artificial neural network with varying input parameter
Envrionmental impact assessment of urban development plan by vulnerability model application
International Journal of Environmental Science & Technology Vol., No., pp. 7-, Spring Environmental impact assessment Envrionmental impact assessment of urban development plan by vulnerability model application
Dr. S.SURIYA. Assistant professor. Department of Civil Engineering. B. S. Abdur Rahman University. Chennai
Hydrograph simulation for a rural watershed using SCS curve number and Geographic Information System Dr. S.SURIYA Assistant professor Department of Civil Engineering B. S. Abdur Rahman University Chennai
RAINFALL RUNOFF MODELING USING SUPPORT VECTOR REGRESSION AND ARTIFICIAL NEURAL NETWORKS
CEST2011 Rhodes, Greece Ref no: XXX RAINFALL RUNOFF MODELING USING SUPPORT VECTOR REGRESSION AND ARTIFICIAL NEURAL NETWORKS D. BOTSIS1 1, P. LATINOPOULOS 2 and K. DIAMANTARAS 3 1&2 Department of Civil
Comparison learning algorithms for artificial neural network model for flood forecasting, Chiang Mai, Thailand
22nd International Congress on Modelling and Simulation, Hobart, Tasmania, Australia, 3 to 8 December 2017 mssanz.org.au/modsim2017 Comparison learning algorithms for artificial neural network model for
A Neural Network Scheme For Earthquake. City University, Centre for Information Engineering(1), Department of Computer Science(2),
A Neural Network Scheme For Earthquake Prediction Based On The Seismic Electric Signals Spiros Lakkos 1, Andreas Hadjiprocopis 2, Richard Comley 1, Peter Smith 2 City University, Centre for Information
Floodplain modeling. Ovidius University of Constanta (P4) Romania & Technological Educational Institute of Serres, Greece
Floodplain modeling Ovidius University of Constanta (P4) Romania & Technological Educational Institute of Serres, Greece Scientific Staff: Dr Carmen Maftei, Professor, Civil Engineering Dept. Dr Konstantinos
FLOODPLAIN MAPPING OF RIVER KRISHNANA USING HEC-RAS MODEL AT TWO STREACHES NAMELY KUDACHI AND UGAR VILLAGES OF BELAGAVI DISTRICT, KARNATAKA
FLOODPLAIN MAPPING OF RIVER KRISHNANA USING HEC-RAS MODEL AT TWO STREACHES NAMELY KUDACHI AND UGAR VILLAGES OF BELAGAVI DISTRICT, KARNATAKA Sandhyarekha 1, A. V. Shivapur 2 1M.tech Student, Dept. of and
Hydrologic Modelling of the Upper Malaprabha Catchment using ArcView SWAT
Hydrologic Modelling of the Upper Malaprabha Catchment using ArcView SWAT Technical briefs are short summaries of the models used in the project aimed at nontechnical readers. The aim of the PES India
Using Empirical Relationships and Neural Network in GIS for Developing Rainfall-Runoff Model
Using Empirical Relationships and Neural Network in GIS for Developing Rainfall-Runoff Model Shahrbanu FIRUZI, Mohamadbagher SHARIFI, Kambiz BORNA and MohamadReza RAJABI, Iran Key words: Run-off, Artificial
Applying GIS to Hydraulic Analysis
Texas A&M University Department of Civil Engineering CVEN689 Applications of GIS to Civil Engineering Instructor: Francisco Olivera, Ph.D., P.E. Applying GIS to Hydraulic Analysis Lim, Chae Kwan April
Multilayer Perceptron Learning Utilizing Singular Regions and Search Pruning
Multilayer Perceptron Learning Utilizing Singular Regions and Search Pruning Seiya Satoh and Ryohei Nakano Abstract In a search space of a multilayer perceptron having hidden units, MLP(), there exist
Journal of Urban and Environmental Engineering, v.3, n.1 (2009) 1 6 ISSN doi: /juee.2009.v3n
J U E E Journal of Urban and Environmental Engineering, v.3, n.1 (2009) 1 6 ISSN 1982-3932 doi: 10.4090/juee.2009.v3n1.001006 Journal of Urban and Environmental Engineering www.journal-uee.org USING ARTIFICIAL
Introduction to Natural Computation. Lecture 9. Multilayer Perceptrons and Backpropagation. Peter Lewis
Introduction to Natural Computation Lecture 9 Multilayer Perceptrons and Backpropagation Peter Lewis 1 / 25 Overview of the Lecture Why multilayer perceptrons? Some applications of multilayer perceptrons.
Forecasting of Rain Fall in Mirzapur District, Uttar Pradesh, India Using Feed-Forward Artificial Neural Network
International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 2 Issue 8ǁ August. 2013 ǁ PP.87-93 Forecasting of Rain Fall in Mirzapur District, Uttar Pradesh,
Index of drought and wet years using the percentage of normal (PNPI) and Nietzsche Semnan
Journal of Biodiversity and Environmental Sciences (JBES) ISSN: 2220-6663 (Print) 2222-3045 (Online) Vol. 4, No. 6, p. 124-130, 2014 http://www.innspub.net RESEARCH PAPER OPEN ACCESS Index of drought and
Multivariate Analysis, TMVA, and Artificial Neural Networks
http://tmva.sourceforge.net/ Multivariate Analysis, TMVA, and Artificial Neural Networks Matt Jachowski jachowski@stanford.edu 1 Multivariate Analysis Techniques dedicated to analysis of data with multiple
Remote sensing of sealed surfaces and its potential for monitoring and modeling of urban dynamics
Remote sensing of sealed surfaces and its potential for monitoring and modeling of urban dynamics Frank Canters CGIS Research Group, Department of Geography Vrije Universiteit Brussel Herhaling titel van
Evaluating the Performance of Artificial Neural Network Model in Downscaling Daily Temperature, Precipitation and Wind Speed Parameters
Int. J. Environ. Res., 8(4):1223-1230, Autumn 2014 ISSN: 1735-6865 Evaluating the Performance of Artificial Neural Network Model in Downscaling Daily Temperature, Precipitation and Wind Speed Parameters
Journal of of Computer Applications Research Research and Development and Development (JCARD), ISSN (Print), ISSN
JCARD Journal of of Computer Applications Research Research and Development and Development (JCARD), ISSN 2248-9304(Print), ISSN 2248-9312 (JCARD),(Online) ISSN 2248-9304(Print), Volume 1, Number ISSN
Accuracy improvement program for VMap1 to Multinational Geospatial Co-production Program (MGCP) using artificial neural networks
7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Edited by M. Caetano and M. Painho. Accuracy improvement program for VMap1 to Multinational Geospatial
Application of Artificial Neural Networks to Predict Daily Solar Radiation in Sokoto
Research Article International Journal of Current Engineering and Technology ISSN 2277-4106 2013 INPRESSCO. All Rights Reserved. Available at http://inpressco.com/category/ijcet Application of Artificial
Runoff Estimation and Mapping within GIS Based Arc-CN-Runoff
Advances in Bioresearch Adv. Biores., Vol 6 (3) May 2015: 16-20 2015 Society of Education, India Print ISSN 0976-4585; Online ISSN 2277-1573 Journal s URL:http://www.soeagra.com/abr.html CODEN: ABRDC3
Investigation of Relationship Between Rainfall and Vegetation Index by Using NOAA/AVHRR Satellite Images
World Applied Sciences Journal 14 (11): 1678-1682, 2011 ISSN 1818-4952 IDOSI Publications, 2011 Investigation of Relationship Between Rainfall and Vegetation Index by Using NOAA/AVHRR Satellite Images
Effects of different methods for estimating impervious surface cover on runoff estimation at catchment level
Effects of different methods for estimating impervious surface cover on runoff estimation at catchment level Frank Canters 1, Jarek Chormanski 2, Tim Van de Voorde 1 and Okke Batelaan 2 1 Department of
MODULE 8 LECTURE NOTES 2 REMOTE SENSING APPLICATIONS IN RAINFALL-RUNOFF MODELLING
MODULE 8 LECTURE NOTES 2 REMOTE SENSING APPLICATIONS IN RAINFALL-RUNOFF MODELLING 1. Introduction The most common application of the remote sensing techniques in the rainfall-runoff studies is the estimation
Delineation of Groundwater Potential Zone on Brantas Groundwater Basin
Delineation of Groundwater Potential Zone on Brantas Groundwater Basin Andi Rachman Putra 1, Ali Masduqi 2 1,2 Departement of Environmental Engineering, Sepuluh Nopember Institute of Technology, Indonesia
Artificial Neural Networks
Introduction ANN in Action Final Observations Application: Poverty Detection Artificial Neural Networks Alvaro J. Riascos Villegas University of los Andes and Quantil July 6 2018 Artificial Neural Networks
Journal of Water and Soil Vol. 26, No.4, Sept.-Oct. 2012, p ( : -*)
Journal of Water and Soil Vol. 26, No.4, Sept.-Oct. 2012, p. 933-942 ( ) 933-942. 1391 4 26 4 3 2 *1 - - - 90 /6/8: 91/3/20: -... 15. GP. GP. GP. :. :..(2) (16). - (12). (3)... 1.. ( ).(1). - -4 3 2 1
Advanced Image Analysis in Disaster Response
Advanced Image Analysis in Disaster Response Creating Geographic Knowledge Thomas Harris ITT The information contained in this document pertains to software products and services that are subject to the
ESTIMATION OF HOURLY MEAN AMBIENT TEMPERATURES WITH ARTIFICIAL NEURAL NETWORKS 1. INTRODUCTION
Mathematical and Computational Applications, Vol. 11, No. 3, pp. 215-224, 2006. Association for Scientific Research ESTIMATION OF HOURLY MEAN AMBIENT TEMPERATURES WITH ARTIFICIAL NEURAL NETWORKS Ömer Altan
Creation of high resolution soil parameter data by use of artificial neural network technologies (advangeo )
Creation of high resolution soil parameter data by use of artificial neural network technologies (advangeo ) A. Knobloch 1, F. Schmidt 1, M.K. Zeidler 1, A. Barth 1 1 Beak Consultants GmbH, Freiberg /
Review Using the Geographical Information System and Remote Sensing Techniques for Soil Erosion Assessment
Polish J. of Environ. Stud. Vol. 19, No. 5 (2010), 881-886 Review Using the Geographical Information System and Remote Sensing Techniques for Soil Erosion Assessment Nuket Benzer* Landscape Architecture
Sediment- yield estimation, by M-PSIAC method in a GIS environment, case study:jonaghn river sub basin(karun basin)
Sediment- yield estimation, by M-PSIAC method in a GIS environment, case study:jonaghn river sub basin(karun basin) Yavari-shahla *,Khdabakhsh-Saeed,Mohseni-Hasan, Rezai- Khalil Corresponding author: a
Prediction of Monthly Rainfall of Nainital Region using Artificial Neural Network (ANN) and Support Vector Machine (SVM)
Vol- Issue-3 25 Prediction of ly of Nainital Region using Artificial Neural Network (ANN) and Support Vector Machine (SVM) Deepa Bisht*, Mahesh C Joshi*, Ashish Mehta** *Department of Mathematics **Department
Susquehanna River Basin A Research Community Hydrologic Observatory. NSF-Funded Infrastructure Proposal in Support of River Basin Hydrologic Sciences
Susquehanna River Basin A Research Community Hydrologic Observatory NSF-Funded Infrastructure Proposal in Support of River Basin Hydrologic Sciences Fundamental Problem: How Do Humans and Climate Impact
y(x n, w) t n 2. (1)
Network training: Training a neural network involves determining the weight parameter vector w that minimizes a cost function. Given a training set comprising a set of input vector {x n }, n = 1,...N,
Distinct landscape features with important biologic, hydrologic, geomorphic, and biogeochemical functions.
1 Distinct landscape features with important biologic, hydrologic, geomorphic, and biogeochemical functions. Have distinguishing characteristics that include low slopes, well drained soils, intermittent
Application of an Artificial Neural Network Based Tool for Prediction of Pavement Performance
0 0 0 0 Application of an Artificial Neural Network Based Tool for Prediction of Pavement Performance Adelino Ferreira, Rodrigo Cavalcante Pavement Mechanics Laboratory, Research Center for Territory,
Linking Arid Land Surface Characteristics to Soil Hydrologic and Ecosystem Functions in Mojave Desert Landscapes
2006-2011 Mission Kearney Foundation of Soil Science: Understanding and Managing Soil-Ecosystem Functions Across Spatial and Temporal Scales Progress Report: 2006022, 1/1/2007-12/31/2007 Linking Arid Land
A Hybrid ARIMA and Neural Network Model to Forecast Particulate. Matter Concentration in Changsha, China
A Hybrid ARIMA and Neural Network Model to Forecast Particulate Matter Concentration in Changsha, China Guangxing He 1, Qihong Deng 2* 1 School of Energy Science and Engineering, Central South University,
Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting
Journal of Physics: Conference Series PAPER OPEN ACCESS Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting To cite this article: IP Sutawinaya et al 2018 J. Phys.: Conf.
Three Structures of a Multilayer Artificial Neural Network for Predicting the Solar Radiation of Baghdad City- Iraq
Three Structures of a Multilayer Artificial Neural Network for Predicting the Solar Radiation of Baghdad City- Iraq a,* Abdulrahman Th. Mohammad, b khalil I. Mahmood, c Dawood S. Mahjoob a,b,c Baqubah
Using object oriented technique to extract jujube based on landsat8 OLI image in Jialuhe Basin
Journal of Image Processing Theory and Applications (2016) 1: 16-20 Clausius Scientific Press, Canada Using object oriented technique to extract jujube based on landsat8 OLI image in Jialuhe Basin Guotao
Integrated Remote Sensing and GIS Approach for Groundwater Exploration using Analytic Hierarchy Process (AHP) Technique.
ISSN (Online) : 2319-8753 ISSN (Print) : 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology An ISO 3297: 2007 Certified Organization, Volume 2, Special Issue
Neural Network Identification of Non Linear Systems Using State Space Techniques.
Neural Network Identification of Non Linear Systems Using State Space Techniques. Joan Codina, J. Carlos Aguado, Josep M. Fuertes. Automatic Control and Computer Engineering Department Universitat Politècnica
Multilayer Perceptrons and Backpropagation
Multilayer Perceptrons and Backpropagation Informatics 1 CG: Lecture 7 Chris Lucas School of Informatics University of Edinburgh January 31, 2017 (Slides adapted from Mirella Lapata s.) 1 / 33 Reading:
Desertification Hazard Zonation by Means of ICD Method in Kouhdasht Watershed
DESERT DESERT Online at http://jdesert.ut.ac.ir DESERT 17 (13) 233240 Desertification Hazard Zonation by Means of ICD Method in Kouhdasht Watershed Gh. Chamanpira a*, Gh.R. Zehtabian b, H. Ahmadi c, M.
APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR FLOOD FORECASTING
Global Nest: the Int. J. Vol 6, No 3, pp 4-1, 4 Copyright 4 GLOBAL NEST Printed in Greece. All rights reserved APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR FLOOD FORECASTING D.F. LEKKAS 1,* 1 Department
Neural Networks and the Back-propagation Algorithm
Neural Networks and the Back-propagation Algorithm Francisco S. Melo In these notes, we provide a brief overview of the main concepts concerning neural networks and the back-propagation algorithm. We closely
Overview of Remote Sensing in Natural Resources Mapping
Overview of Remote Sensing in Natural Resources Mapping What is remote sensing? Why remote sensing? Examples of remote sensing in natural resources mapping Class goals What is Remote Sensing A remote sensing
A Logarithmic Neural Network Architecture for Unbounded Non-Linear Function Approximation
1 Introduction A Logarithmic Neural Network Architecture for Unbounded Non-Linear Function Approximation J Wesley Hines Nuclear Engineering Department The University of Tennessee Knoxville, Tennessee,
Applications of Artificial Neural Network in Determining the Mechanical Properties of Melon Fruits
IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS) e-issn: 2319-2380, p-issn: 2319-2372. Volume 6, Issue 6 (Jan. 2014), PP 12-16 Applications of Artificial Neural Network in Determining the
A distributed runoff model for flood prediction in ungauged basins
Predictions in Ungauged Basins: PUB Kick-off (Proceedings of the PUB Kick-off meeting held in Brasilia, 2 22 November 22). IAHS Publ. 39, 27. 267 A distributed runoff model for flood prediction in ungauged
Application of GIS and Remote Sensing in Watershed Restoration
Valerie Preler December 15, 2015 NRS 509 final Project Application of GIS and Remote Sensing in Watershed Restoration Over fifty years ago, Lyndon B. Johnson was once said A nation that fails to plan intelligently
Ajoke Onojeghuo & Alex Onojeghuo (Nigeria)
Mapping and Predicting Urban Sprawl Using Remote Sensing and Geographic Information System Techniques: A Case Study of Eti-Osa Local Government Area, Lagos, Nigeria Ajoke Onojeghuo & Alex Onojeghuo (Nigeria)
RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS
RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS CHANDRASHEKAR SUBRAMANIAN MICHAEL T. MANRY Department Of Electrical Engineering The University Of Texas At Arlington Arlington, TX 7619 JORGE NACCARINO
Nutrient and Sediment Cycling and Retention in Urban Floodplain Wetlands
Nutrient and Sediment Cycling and Retention in Urban Floodplain Wetlands Greg Noe, Cliff Hupp, Nancy Rybicki, Ed Schenk, and Jackie Batson National Research Program, Reston VA U.S. Department of the Interior
SPACE & UPPER ATMOSPHERE RESEARCH COMMISSION (SUPARCO)
SPACE & UPPER ATMOSPHERE RESEARCH COMMISSION (SUPARCO) Pakistan Space & Upper Atmosphere Research Commission (SUPARCO) is the national space agency, responsible for execution of space technology ad applications
Using satellite images to calculate land use and land cover statistics
Using satellite images to calculate land use and land cover statistics National Administrative Department of Statistics DANE Colombia April 2016 @DANE_Colombia /DANEColombia /DANEColombia Background (1)
Flood zoning estimation and river management by using HEC-RAS and GIS model
Int. J. Energ. Water Resour., 1(1): 13-18, Winter 2017 IRSEN, IAU Flood zoning estimation and river management by using HEC-RAS and GIS model A.R. Mardookhpour*, H. Jamasbi Department of Civil Engineering,
A Hybrid Model of Wavelet and Neural Network for Short Term Load Forecasting
International Journal of Electronic and Electrical Engineering. ISSN 0974-2174, Volume 7, Number 4 (2014), pp. 387-394 International Research Publication House http://www.irphouse.com A Hybrid Model of
A multi-stage methodology for selecting input variables in ANN forecasting of river flows
Global NEST Journal, Vol 19, No 1, pp 49-57 Copyright 2017 Global NEST Printed in Greece. All rights reserved A multi-stage methodology for selecting input variables in ANN forecasting of river flows Panagoulia
Examining the gold coin market of Iran in the rational expectations framework using the artificial neural network
Examining the gold coin market of Iran in the rational expectations framework using the artificial neural network Mohammad Kavoosi-Kalashami Assistant Professor, Department of Agricultural Economics, Faculty
What Do Neural Networks Do? MLP Lecture 3 Multi-layer networks 1
What Do Neural Networks Do? MLP Lecture 3 Multi-layer networks 1 Multi-layer networks Steve Renals Machine Learning Practical MLP Lecture 3 7 October 2015 MLP Lecture 3 Multi-layer networks 2 What Do Single
Estimation of extreme flow quantiles and quantile uncertainty for ungauged catchments
Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007. 417 Estimation
CE 394K.3 GIS in Water Resources Midterm Quiz Fall There are 5 questions on this exam. Please do all 5. They are of equal credit.
Name: CE 394K.3 GIS in Water Resources Midterm Quiz Fall 2000 There are 5 questions on this exam. Please do all 5. They are of equal credit. 1. The ArcView Geographic Information System can display different
Lesson 4b Remote Sensing and geospatial analysis to integrate observations over larger scales
Lesson 4b Remote Sensing and geospatial analysis to integrate observations over larger scales We have discussed static sensors, human-based (participatory) sensing, and mobile sensing Remote sensing: Satellite
INTRODUCING A NEW FORMULA BASED ON AN ARTIFICIAL NEURAL NETWORK FOR PREDICTION OF DROPLET SIZE IN VENTURI SCRUBBERS
Brazilian Journal of Chemical Engineering ISSN 0104-6632 Printed in Brazil www.abeq.org.br/bjche Vol. 29, No. 03, pp. 549-558, July - September, 2012 INTRODUCING A NEW FORMULA BASED ON AN ARTIFICIAL NEURAL
Watershed Delineation in GIS Environment Rasheed Saleem Abed Lecturer, Remote Sensing Centre, University of Mosul, Iraq
Watershed Delineation in GIS Environment Rasheed Saleem Abed Lecturer, Remote Sensing Centre, University of Mosul, Iraq Abstract: The management and protection of watershed areas is a major issue for human
This is trial version
Journal of Rangeland Science, 2012, Vol. 2, No. 2 J. Barkhordari and T. Vardanian/ 459 Contents available at ISC and SID Journal homepage: www.rangeland.ir Full Paper Article: Using Post-Classification
Influence of Micro-Climate Parameters on Natural Vegetation A Study on Orkhon and Selenge Basins, Mongolia, Using Landsat-TM and NOAA-AVHRR Data
Cloud Publications International Journal of Advanced Remote Sensing and GIS 2013, Volume 2, Issue 1, pp. 160-172, Article ID Tech-102 ISSN 2320-0243 Research Article Open Access Influence of Micro-Climate
Address for Correspondence
Research Article APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR INTERFERENCE STUDIES OF LOW-RISE BUILDINGS 1 Narayan K*, 2 Gairola A Address for Correspondence 1 Associate Professor, Department of Civil
2 Development of a Physically Based Hydrologic Model of the Upper Cosumnes Basin
2 Development of a Physically Based Hydrologic Model of the Upper Cosumnes Basin 2.1 Introduction The physically based watershed hydrologic model, WEHY (watershed environmental hydrology) model (Kavvas
Forecasting Drought in Tel River Basin using Feed-forward Recursive Neural Network
2012 International Conference on Environmental, Biomedical and Biotechnology IPCBEE vol.41 (2012) (2012) IACSIT Press, Singapore Forecasting Drought in Tel River Basin using Feed-forward Recursive Neural
Comparison of three back-propagation training algorithms for two case studies
Indian Journal of Engineering & Materials Sciences Vol. 2, October 2005, pp. 434-442 Comparison of three back-propagation training algorithms for two case studies Özgür Kişi a & Erdal Uncuoğlu b a Hydraulics
GIS and Remote Sensing
Spring School Land use and the vulnerability of socio-ecosystems to climate change: remote sensing and modelling techniques GIS and Remote Sensing Katerina Tzavella Project Researcher PhD candidate Technology
Modeling Soil Temperature Using Artificial Neural Network
2014 5th International Conference on Environmental Science and Technology IPCBEE vol.69 (2014) (2014) IACSIT Press, Singapore DOI: 10.7763/IPCBEE. 2014. V69. 3 Modeling Soil Temperature Using Artificial
River Flow Forecasting with ANN
River Flow Forecasting with ANN OMID BOZORG HADDAD, FARID SHARIFI, SAEED ALIMOHAMMADI Department of Civil Engineering Iran University of Science & Technology, Shahid Abbaspour University Narmak, Tehran,
Geospatial Approach for Delineation of Landslide Susceptible Areas in Karnaprayag, Chamoli district, Uttrakhand, India
Geospatial Approach for Delineation of Landslide Susceptible Areas in Karnaprayag, Chamoli district, Uttrakhand, India Ajay Kumar Sharma & Anand Mohan Singh Overview Landslide - movement of a mass of rock,
Unit III. A Survey of Neural Network Model
Unit III A Survey of Neural Network Model 1 Single Layer Perceptron Perceptron the first adaptive network architecture was invented by Frank Rosenblatt in 1957. It can be used for the classification of
ARTIFICIAL NEURAL NETWORK PART I HANIEH BORHANAZAD
ARTIFICIAL NEURAL NETWORK PART I HANIEH BORHANAZAD WHAT IS A NEURAL NETWORK? The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided
Predicting Wheat Production in Iran Using an Artificial Neural Networks Approach
Predicting Wheat Production in Iran Using an Artificial Neural Networks Approach Reza Ghodsi, Ruzbeh Mirabdollah Yani, Rana Jalali, Mahsa Ruzbahman Industrial Engineering Department, University of Tehran,
APPLICATION OF LAND CHANGE MODELER FOR PREDICTION OF FUTURE LAND USE LAND COVER A CASE STUDY OF VIJAYAWADA CITY
APPLICATION OF LAND CHANGE MODELER FOR PREDICTION OF FUTURE LAND USE LAND COVER A CASE STUDY OF VIJAYAWADA CITY K. Sundara Kumar 1, Dr. P. Udaya Bhaskar 2, Dr. K. Padmakumari 3 1 Research Scholar, 2,3
LANDSLIDE FEATURES INTERPRETED BY NEURAL NETWORK METHOD USING A HIGH-RESOLUTION SATELLITE IMAGE AND DIGITAL TOPOGRAPHIC DATA
LANDSLIDE FEATURES INTERPRETED BY NEURAL NETWORK METHOD USING A HIGH-RESOLUTION SATELLITE IMAGE AND DIGITAL TOPOGRAPHIC DATA K. T. Chang a * and J. K. Liu b a Dept. of Civil Eng., MUST, No. 1, Hsin-Hsing
Determination of flood risks in the yeniçiftlik stream basin by using remote sensing and GIS techniques
Determination of flood risks in the yeniçiftlik stream basin by using remote sensing and GIS techniques İrfan Akar University of Atatürk, Institute of Social Sciences, Erzurum, Turkey D. Maktav & C. Uysal
Approximate solutions of dual fuzzy polynomials by feed-back neural networks
Available online at wwwispacscom/jsca Volume 2012, Year 2012 Article ID jsca-00005, 16 pages doi:105899/2012/jsca-00005 Research Article Approximate solutions of dual fuzzy polynomials by feed-back neural
Optimal Artificial Neural Network Modeling of Sedimentation yield and Runoff in high flow season of Indus River at Besham Qila for Terbela Dam
Optimal Artificial Neural Network Modeling of Sedimentation yield and Runoff in high flow season of Indus River at Besham Qila for Terbela Dam Akif Rahim 1, Amina Akif 2 1 Ph.D Scholar in Center of integrated
Comparing the Land Use and Land Cover Changes Between the North and South Louisiana Using Data Mining
Comparing the Land Use and Land Cover Changes Between the North and South Louisiana Using Data Mining Yi Qiang and Nina Lam Department of Environmental Sciences Louisiana State University yqiang1@lsu.edu,
Deep Neural Networks (1) Hidden layers; Back-propagation
Deep Neural Networs (1) Hidden layers; Bac-propagation Steve Renals Machine Learning Practical MLP Lecture 3 4 October 2017 / 9 October 2017 MLP Lecture 3 Deep Neural Networs (1) 1 Recap: Softmax single
Ocean Based Water Allocation Forecasts Using an Artificial Intelligence Approach
Ocean Based Water Allocation Forecasts Using an Artificial Intelligence Approach Khan S 1, Dassanayake D 2 and Rana T 2 1 Charles Sturt University and CSIRO Land and Water, School of Science and Tech,
A Review on Artificial Neural Networks Modeling for Suspended Sediment Estimation
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) e-issn: 2321 0990, p-issn: 2321 0982.Volume 4, Issue 3 Ver. II (May. - Jun. 2016), PP 34-39 www.iosrjournals.org A Review on Artificial Neural
A "consensus" real-time river flow forecasting model for the Blue Nile River
82 Water Resources Systems--Hydrological Risk, Management and Development (Proceedings of symposium HS02b held during IUGG2003 al Sapporo. July 2003). IAHS Publ. no. 281. 2003. A "consensus" real-time
The Impact of Vegetation on the Bank Erosion (Case Study: The Haraz River)
The Impact of Vegetation on the Bank Erosion (Case Study: The Haraz River) Vahid GHOLAMI 1 and Mohamad Reza KHALEGHI 2 1 Department of Range and Watershed Management, Faculty of Natural Resources, University
KINEROS2/AGWA. Fig. 1. Schematic view (Woolhiser et al., 1990).
KINEROS2/AGWA Introduction Kineros2 (KINematic runoff and EROSion) (K2) model was originated at the USDA-ARS in late 1960s and released until 1990 (Smith et al., 1995; Woolhiser et al., 1990). The spatial
Predicting the input flow into the Dez dam reservoir using the optimized neural network by genetic algorithm
, Pages: x-x TI Journals International Journal of Engineering Sciences www.tijournals.com ISSN 2306-6474 Predicting the input flow into the Dez dam reservoir using the optimized neural network by genetic