The Economic Evaluation of Optimal Water Allocation Using Artificial Neural Network (Case Study: Moghan Plain)

Authors

1 Department of Agricultural Economics, Faculty of Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran.

2 Department of Water Engineering, Faculty of Agriculture, Tabriz, Iran.

Abstract

recipitation shortage and the consequent loss of several water resources, as well as the population growth, are the most important problems in arid and semi-arid regions like Iran. The providence of basic tools for optimal water resources management is considered as one of the main solutions to this problem. Since the agricultural sector is the main user of water resources, the present study presented a model based on an artificial neural network method for optimal allocation of water resources in the agricultural sector during the statistical period of 2007-2016. The objective function was determined for each product in the agricultural sector as well as product performance, each product revenues, and cultivated area of the demand function. Maximization of the objective function (to maximize economic profits) and optimal allocation of water resources were; then, conducted by using the neural network. The results of the application of the artificial neural network method to the problem of optimal water allocation showed that, in this section, higher revenues could be obtained through economic policies as well as changing the pattern of cultivation. Furthermore, the results revealed that about 44 percent of the optimal allocation revenues of water resources ($115 billion) were improved between the agricultural sectors, compared to the current situation, by applying a coefficient of 0.9 compared to two coefficients of 0.75.
 

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