INVESTIGATION OF THE LAND POTENTIAL OF KERMANSHAH PROVINCE FOR RAINFED WHEAT CULTIVATION USING ARTIFICIAL NEURAL NETWORK

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BAGHERI M.; JELOKHANI NIYARAKI M.R.; BAGHERI K.

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Journal: Journal of RS and GIS for Natural Resources Year:2018 | Volume:8 | Issue:4 (29) Page(s): 36-48

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Title

INVESTIGATION OF THE LAND POTENTIAL OF KERMANSHAH PROVINCE FOR RAINFED WHEAT CULTIVATION USING ARTIFICIAL NEURAL NETWORK

Author(s)

BAGHERI M. | JELOKHANI NIYARAKI M.R. | BAGHERI K. | Issue Writer Certificate

Keywords

WHEATQ3

NEURAL NETWORKSQ2

ZONINGQ2

MULTILAYER PERCEPTRON (MLP)Q1

Abstract

With increasing population growth and the need for food, WHEAT as the crop with the largest cultivated area and annual production on a global scale has been especially important. Therefore, identifying and recommending suitable areas for cultivation in each area is essential. Kermanshah province as the study area is one of the areas that most WHEAT crops are from among. Therefore, in this study Multilayer Perceptron Neural Network (MLP) with Levenberg-Marquardt algorithm was used to identify the potential of rainfed WHEAT cultivation. The input layer network consists of 12 layers: land use, average annual rainfall, average rainfall in the autumn, the average spring rainfall, the average annual temperature, average temperatures in spring, average temperatures in autumn, slope, aspect, elevation, humidity the relative and degree of days. The rainfall and temperature layers were prepared using the data from the stations of adventurous and synoptic and the interpolation operation in the ArcGIS environment, respectively.The altitude-related layer was extracted using with a DEM 30×30 meter IRS. To determine the search space of the neural network algorithm, the uncultivated areas are determined and removed from the entire input layers. 210 points of The right place to cultivate were prepared as network training points. Finally the class of uncultivated areas which 15%, and The results of the model consists of five classes: very suitable, suitable, somewhat suitable, poor or very poor, respectively, 5.4, 14.8, 24, 22.5 and 18.3 percent of the total area of the province is allocated. Regression analysis of all data on the network is 91% of the network of the company, effective for the MLP neural network is in these ZONING.

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APA: Copy

BAGHERI, M., JELOKHANI NIYARAKI, M.R., & BAGHERI, K.. (2018). INVESTIGATION OF THE LAND POTENTIAL OF KERMANSHAH PROVINCE FOR RAINFED WHEAT CULTIVATION USING ARTIFICIAL NEURAL NETWORK. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), 8(4 (29) ), 36-48. SID. https://sid.ir/paper/189487/en

Vancouver: Copy

BAGHERI M., JELOKHANI NIYARAKI M.R., BAGHERI K.. INVESTIGATION OF THE LAND POTENTIAL OF KERMANSHAH PROVINCE FOR RAINFED WHEAT CULTIVATION USING ARTIFICIAL NEURAL NETWORK. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)[Internet]. 2018;8(4 (29) ):36-48. Available from: https://sid.ir/paper/189487/en

IEEE: Copy

M. BAGHERI, M.R. JELOKHANI NIYARAKI, and K. BAGHERI, “INVESTIGATION OF THE LAND POTENTIAL OF KERMANSHAH PROVINCE FOR RAINFED WHEAT CULTIVATION USING ARTIFICIAL NEURAL NETWORK,” JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), vol. 8, no. 4 (29) , pp. 36–48, 2018, [Online]. Available: https://sid.ir/paper/189487/en

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