Agent-based conceptual development and optimization based on a genetic algorithm in water resource allocation

In water resource allocation, a good division is a major principle which is difficult to determine due to the existence of different criteria. To optimize water resource allocation, it would be efficient to simulate water resource systems in order to consider effective agents and reveal the internal interaction among their parts. Various studies show that a multi-agent simulation alone, or in combination with optimization methods, is an effective approach for understanding better the complexities related to water use and users. Also, the genetic algorithm has received attention as an intelligent evolutionary method to optimize non-linear complex problems. The conceptual framework of the proposed water resource allocation presented the interaction between water demand and supply, taking into consideration the economic factors in a sub-basin of Dasht-e Kavir desert in Iran, whose major water source is groundwater. One of the most important duties of water allocators is to achieve optimized allocation of water to different sectors, performed on the basis of the water demands of each consuming agent. Agricultural agents who receive the major portion of water were divided into sub-units. For each product, the diversity of cultivation patterns, deficit irrigation conditions, etc. were considered in order to improve economic status and allocate water resources optimally based on available data and statistics. In industrial uses, products and their functions were discussed as a function governing all businesses. Finally, as water supply is especially important in the drinking sector, the total volume of water required was calculated and completely allocated for this. In the study area, the cultivation of fodder and oil plants is not optimal on the basis of the available water resources with the criterion of maximizing economical profit. Cereals, followed by fruit- bearing trees (including pistachio, pomegranate, grape, and date) have the largest area under cultivation. Results showed that cereals retain their large cultivation area due to deficit irrigation, and the increase in the area under cultivation belonging to garden products is because of their high profitability. Therefore, in the agricultural sector, water allocation can be optimized by using deficit irrigation in cereals and changing the cultivation pattern for products relating to fodder and oil plants. In the industrial sector, the important point is the changing impact of technology on reducing water demand. Since this sector has a higher economical profitability than the agricultural sector, optimized allocation in order to increase economical profitability has led to a water allocation higher than the current consumption level. Evaluation of the optimization results in the genetic algorithm indicates that the convergence rate is high in first iterations and gradually decreases to reach convergence. The convergence of the optimization function is achieved gradually. Moreover, the small variance of changes in the final output of the algorithm (ranging from 0 to 1) suggests the high stability of this algorithm. Implementation of the proposed framework in the study area increases the economic profitability resulting from optimized water resource allocation to various sectors, if a move is observed from low-efficiency agricultural products to high-efficiency garden products, and the higher allocation of water to industry.