Parametric-based neural networks and TOPSIS modeling in land suitability evaluation for alfalfa production using GIS

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• 作者：Ali Bagherzadeh ; Amin Gholizadeh
• 关键词：Land suitability ; Evaluation ; Alfalfa ; Neural networks ; TOPSIS ; GIS
• 刊名：Modeling Earth Systems and Environment
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：3
• 期：1
• 全文大小：1052KB
• 刊物类别：Earth System Sciences; Math. Appl. in Environmental Science; Statistics for Engineering, Physics, Co
• 刊物主题：Earth System Sciences; Math. Appl. in Environmental Science; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Mathematical Applications in the Physical Sciences; Ec
• 出版者：Springer International Publishing
• ISSN：2363-6211
• 卷排序：3

文摘

Land evaluation is the process of predicting land use potential on the basis of its attributes. In the present study, the qualitative land suitability evaluation using parametric based neural networks and TOPSIS models was investigated for irrigated alfalfa production in Joveyn plain, Northeast of Iran. Some twenty-six land units were studied at the study area by a precise soil survey and their morphological and physicochemical properties. Our results indicated that the most limiting factor for alfalfa cultivation in the study area was soil fertility properties. The values of land indexes by neural networks model ranged from 46.39 in some parts in east and west to 75.91 in the middle parts of the study area, which categorized the plain from moderate (S3) to high (S1) suitable classes. The TOPSIS preference values for alfalfa cultivation in the study area varied between 0.388 and 0.773 which classified from moderate to very high classes. The coefficient of determination revealed a high correlation between the output results of two models (R2 = 0.961).