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Manuscript ID : JZPM-1704-2615 (R1) Visit : 70 Page: 15 - 30

20.1001.1.22516735.1397.8.30.2.2

Article Type: Original Research

Measuring and Analyzing Agricultural Development of Iran Using Artificial Neural Network

Subject Areas : Regional Planning

آیت اله کرمی 1 , حمید رستگاری 2

1 - هیئت علمی دانشگاه یاسوج
2 - دانشجوی دکتری دانشگاه یاسوج

Received: 2017-04-29 Accepted : 2018-05-09 Published : 2018-06-22

Keywords: Iran, Agricultural Development, Self-Organization Map, Neural Network Perceptron,

Abstract :

      In general, there is a lack of balanced development in the agricultural sector of many developing countries, including Iran. Therefore, the study of the areas of imbalances and inequalities in the development of this sector is inevitable. By studying the strengths and weaknesses of the provinces, a proper planning can be done. The purpose of this study was to identify the extent of agricultural development in the provinces of Iran. So, the research question was: What is the level of agricultural development in the provinces of Iran? The present study is descriptive-analytic in terms of applied and methodological point of view. The method of collecting information was documentary, library, and standardized tools in the form of formal tables and official results of Agricultural Census results. The statistical population of the study was all provinces of Iran (N=31). Then, 73 sub-indicators in the form of five main agricultural development indicators were extracted from the census and after weighing the indices, the artificial neural network was used to study the agricultural development of the provinces. The calculations were done using Excel and MATLAB software. The results showed that Esfahan, Tehran and Mazandaran ranked first to third respectively and Southern Khorasan, Sistan & Baluchistan and Bushehr ranked last in terms of agricultural development. It is necessary to mention that “exploitation agriculture index” in clusters 2 and 3 and “infrastructure services and other agricultural services index” in cluster 1, most importance were accounted.

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