عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Water scarcity is one of the problems to the utilization of water resources in the agriculture sector. Therefore, a principled management of water resources is needed to optimally meet the water requirements of this sector. The Irrigation water allocation management could play an important role in confronting the water scarcity of the agriculture sector. The use of optimization technique to optimally allocate water and land has recently been proposed and used in many studies and it was proved to be very effective in the optimal water allocation problems. Rabie et al. (2015) allocated water to the lands irrigated by the Ordibehesht canal of Doroodzan irrigation network in Fars Province, using genetic algorithm. Results indicated that total cultivated area could be reduced by up to 12% under optimal water allocation situation. The results of the study conducted by Mizaei et al. (2017) on optimizing the cropping pattern of Golestan irrigation network using genetic algorithm, demonstrated that 38% of the available water left as the surplus water after the optimization. This amount of water could increase the cropping area by 1388 hectares. In this study, an optimization model was created to optimally allocate the available water to the Maroon irrigation network, which is located in Khuzestan Province, Iran, using the genetic algorithms optimization method with the purpose of maximizing the total net benefit.
First, the water requirement of all crops in the Maroon irrigation network was calculated according to Allen et al. (1998) using Cropwat 8.0 software. Then, an optimization model was created in order to optimally allocate the irrigation water to the cropping pattern of the Maroon irrigation network using Matlab software. In this model, the water year was divided into 36 periods consisting of 10 days. The amount of irrigation water depth of the crops and their cropping area were considered as the crucial variables of the model. As genetic algorithm has been proved to be effective in the recent optimal irrigation water allocation and cropping pattern studies, the genetic algorithm optimization method was used to allocate the irrigation water to the Maroon irrigation network with the in order to maximize the total net benefit (Faghihi et al., 2015; Rabie et al., 2015; Mirzaei et al., 2017). Furthermore, the particle swarm optimization method was used in order to verify the results obtained from genetic algorithm optimization method. Comparison of the results obtained using the mentioned optimization methods can demonstrate whether the genetic algorithm results are verified or not. In order to achieve the optimal solution, both optimization methods had some parameters to be set. The parameters were set using the Vikor index according to Akbaripour and Masehian (2013).
The total net benefit maximization results obtained from particle swarm optimization method verified the maximization results of total net benefit obtained from the study model using genetic algorithm optimization method. The model’s results indicated that the cropping patterns of all crops are increased except alfalfa and wheat. The total network’s cultivated area is increased by 1271 hectares, which means that 14% of the network’s abandoned area could be cultivated. There are so many lands that were cultivated in the past and is not currently under cultivation because the available water resources cannot meet all of the water requirements of the agriculture sector in the study area. So, some lands were abandoned and are not under cultivation anymore, but the optimal allocation of irrigation water is an efficient strategy to handle the water scarcity challenge in the agricultural sector; and some of these lands could be cultivated again, which leads to a more efficient agriculture sector. Furthermore, the irrigation depth was reduced in all of the crops, but the model compensated the reduction using the available soil moisture. So, the crops were exposed to insignificant amount of water stress and the crops yield is not significantly reduced, however, the amount of total water use is not significantly reduced in comparison with the current irrigation water allocation situation because the network’s total cropping area is increased. The water resources’ allocation optimization lead to increase in total net benefit by 74.9 billion Rials. The results also proved the efficiency of genetic algorithm in this water allocation optimization model by increasing the total cropping area and the network’s total net benefit.