Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 631-638, 2015 http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W5/631/2015/ doi:10.5194/isprsarchives-XL-1-W5-631-2015 © Author(s) 2015. This work is distributedunder the Creative Commons Attribution 3.0 License.

11 Dec 2015
OPTIMUM ALLOCATION OF WATER TO THE CULTIVATION FARMS USING GENETIC ALGORITHM
B. Saeidian, M. Saadi Mesgari, and M. Ghodousi GIS Dept., Faculty of Geodesy and Geomatics Eng., K.N. Toosi University of Tech., Tehran, Iran
Keywords: Agricultural water allocation, Optimization, Genetic algorithm, Deficit irrigation Abstract. The water scarcity crises in the world and specifically in Iran, requires the proper management of this valuable resource. According to the official reports, around 90 percent of the water in Iran is used for agriculture. Therefore, the adequate management and usage of water in this section can help significantly to overcome the above crises. The most important aspect of agricultural water management is related to the irrigation planning, which is basically an allocation problem. The proper allocation of water to the farms is not a simple and trivial problem, because of the limited amount of available water, the effect of different parameters, nonlinear characteristics of the objective function, and the wideness of the solution space. Usually To solve such complex problems, a meta-heuristic method such as genetic algorithm could be a good candidate.

In this paper, Genetic Algorithm (GA) is used for the allocation of different amount of water to a number of farms. In this model, the amount of water transferable using canals of level one, in one period of irrigation is specified. In addition, the amount of water required by each farm is calculated using crop type, stage of crop development, and other parameters. Using these, the water production function of each farm is determined. Then, using the water production function, farm areas, and the revenue and cost of each crop type, the objective function is calculated. This objective function is used by GA for the allocation of water to the farms. The objective function is defined such that the economical profit extracted from all farms is maximized. Moreover, the limitation related to the amount of available water is considered as a constraint. In general, the total amount of allocated water should be less than the finally available water (the water transferred trough the level one canals). Because of the intensive scarcity of water, the deficit irrigation method are considered. In this method, the planning is on the basis of the optimum and limited allocation of water, and not on the basis of the each crop water requirement. According to the available literature, in the condition of water scarcity, the implementation of deficit irrigation strategy results in higher economical income.

The main difference of this research with others is the allocation of water to the farms. Whilst, most of similar researches concentrate on the allocation of water to different water consumption sections (such as agriculture, industry etc.), networks and crops.

Using the GA for the optimization of the water allocation, proper solutions were generated that maximize the total economical income in the entire study area. In addition, although the search space was considerably wide, the results of the implementation showed an adequate convergence speed. The repeatability test of the algorithm also proved that the algorithm is reasonably stable. In general the usage of GA algorithm can be considered as an efficient and trustable method for such irrigation planning problems.

By optimum allocation of the water to the farms with different areas and crop types, and considering the deficit irrigation method, the general income of the entire area can be improved substantially.

Conference paper (PDF, 1034 KB)

Citation: Saeidian, B., Saadi Mesgari, M., and Ghodousi, M.: OPTIMUM ALLOCATION OF WATER TO THE CULTIVATION FARMS USING GENETIC ALGORITHM, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 631-638, doi:10.5194/isprsarchives-XL-1-W5-631-2015, 2015.

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