Integration of artificial neural network and geographical information system for intelligent assessment of land suitability for the cultivation of a selected crop

The main objective of this study is to investigate the potential of artificial neural network (ANN) for integration with geographical information system (GIS) to assess the suitability of land to cultivate a selected crop. For this purpose, requirements of a system for intelligent assessment of land suitably were determined and the architecture of the integrated system was designed according to capabilities of ANN and GIS. For evaluation of the integrated system performance, a case study was carried out in the Mazandaran Province, located in the northern part of Iran. In the system implemented for the case study, four effective parameters in crop cultivation were utilized in the multilayer feed-forward back-propagation neural network analysis. The activation function of the hidden layer was set to log-sigmoid function. Designed ANN was trained using the samples extracted from GIS database, and 4-6-1 (4 input parameters as input layer, 6 neurons in hidden layer and 1 target value representing suitability level) structure shows the best result. Evaluation of the system shows that 83.43 % of ANN’s results are acceptable and consistent with the real world.

[1]  Manoj Khanna,et al.  Land Suitability Analysis for Different Crops: A Multi Criteria Decision Making Approach using Remote Sensing and GIS , 2011 .

[2]  Wenjiang Huang,et al.  Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS , 2011 .

[3]  M. García-Rodríguez,et al.  Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model , 2010 .

[4]  J. Zhou,et al.  Using Genetic Learning Neural Networks for Spatial Decision Making in GIs , 1996 .

[5]  Jonathan Raper,et al.  Spatial evolutionary modeling , 2001, Spatial information systems.

[6]  Abdelkader Mendas,et al.  Integration of MultiCriteria Decision Analysis in GIS to develop land suitability for agriculture: Application to durum wheat cultivation in the region of Mleta in Algeria , 2012 .

[7]  Saro Lee,et al.  Determination and application of the weights for landslide susceptibility mapping using an artificial neural network , 2004 .

[8]  Sukumar Bandopadhyay,et al.  Comparative Evaluation of Neural Network Learning Algorithms for Ore Grade Estimation , 2006 .

[9]  H. Akinci,et al.  Agricultural land use suitability analysis using GIS and AHP technique , 2013 .

[10]  Robert A. Schowengerdt,et al.  A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery , 1995 .

[11]  M. Mokarram,et al.  GIS-BASED MULTICRITERIA LAND SUITABILITY EVALUATION USING ORDERED WEIGHT AVERAGING WITH FUZZY QUANTIFIER : A CASE STUDY IN SHAVUR PLAIN , IRAN , 2011 .

[12]  Abbas Alimohammadi,et al.  A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping , 2010, Comput. Geosci..

[13]  Abbas Alimohammadi,et al.  Landslide Hazard Zonation Using Quantitative Methods in GIS , 2009 .

[14]  R. Brink,et al.  A framework for land evaluation , 1977 .

[15]  E. Van Ranst,et al.  Land suitability assessment for cherimoya in southern Ecuador using expert knowledge and GIS , 1998 .

[16]  B. Pradhan,et al.  Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models , 2010 .

[17]  Roman M Krzanowski,et al.  Evolutionary Modeling of Spatial Information , 2001 .

[18]  MICHAEL G. COLLINS,et al.  Land-Use Suitability Analysis in the United States: Historical Development and Promising Technological Achievements , 2001, Environmental management.

[19]  Cengiz Akbulak.  Analitik hiyerarşi süreci ve coğrafi bilgi sistemleri ile Yukarı Kara Menderes Havzası’nın arazi kullanımı uygunluk analizi , 2010 .

[20]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[21]  Sevgi Yılmaz,et al.  Ardahan Kura Nehri ve Yakın Çevresi Alan Kullanımlarının Belirlenmesi ve Optimal Alan Kullanım Önerileri / Determination of the Land-Uses in the Close Proximity of the River of Kura in Ardahan and Optimal Land Use Proposals , 2011 .

[22]  V. Jayaraman,et al.  Assessment of land suitability potentials for agriculture using a remote sensing and GIS based approach , 2009 .

[23]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[24]  Saro Lee,et al.  Landslide susceptibility analysis using GIS and artificial neural network , 2003 .

[25]  Jacek Malczewski,et al.  GIS-based land-use suitability analysis: a critical overview , 2004 .

[26]  Shattri Mansor,et al.  GIS Based Multicriteria Approaches to Housing Site Suitability Assessment , 2006 .

[27]  M. Perveen,et al.  CROP-LAND SUITABILITY ANALYSIS USING A MULTICRITERIA EVALUATION & GIS APPROACH , 2007 .

[28]  B. Pradhan,et al.  Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks , 2012 .

[29]  Nani Gopal Das,et al.  GIS-based multi-criteria evaluation to land suitability modelling for giant prawn (Macrobrachium rosenbergii) farming in Companigonj Upazila of Noakhali, Bangladesh , 2010 .

[30]  Fangju Wang,et al.  Fuzzy information representation and processing in conventional GIS software: database design and application , 1990, Int. J. Geogr. Inf. Sci..

[31]  L. Ermini,et al.  Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .

[32]  K. P. Sudheer,et al.  Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation , 2008 .

[33]  T N Prakash,et al.  Land Suitability Analysis for Agricultural Crops: A Fuzzy Multicriteria Decision Making Approach , 2003 .

[34]  P. Hosseinzadeh Talaee.  Multilayer perceptron with different training algorithms for streamflow forecasting , 2014 .

[35]  Michael Batty,et al.  From Cells to Cities , 1994 .

[36]  B. Pijanowski,et al.  Using neural networks and GIS to forecast land use changes: a Land Transformation Model , 2002 .

[37]  Thomas Jackson,et al.  Neural Computing - An Introduction , 1990 .

[38]  Thomas Blaschke,et al.  Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS , 2013 .

[39]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[40]  Ruth C. Carter,et al.  Principles , 2003, Law’s Reality.

[41]  Khwanruthai Bunruamkaew,et al.  Site Suitability Evaluation for Ecotourism Using GIS & AHP: A Case Study of Surat Thani Province, Thailand , 2011 .

[42]  D. Kawabata,et al.  Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN) , 2009 .

[43]  T. Kavzoglu,et al.  Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela , 2005 .

[44]  T. Ahamed,et al.  GIS-based fuzzy membership model for crop-land suitability analysis , 2000 .