The Economic Evaluation of Optimal Water Allocation Using Artificial Neural Network (Case Study: Moghan Plain)
نویسندگان
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.
doi
10.22059/ier.2020.77650چکیده
Precipitation shortage, the consequent loss of several water resources, and population growth are the most critical problems in arid and semi-arid regions like Iran. Providing essential tools for optimal water resources management is considered one of the leading solutions to this problem. Since the agricultural sector is the primary user of water resources, the present study presented a model based on an artificial neural network method for the 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, product performance, product revenues, and cultivated area of the demand function. Maximizing the objective function (to maximize economic profits) and optimal allocation of water resources were; then conducted using the neural network. The results of applying 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 and 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.
