Decision support for irrigated project planning using a genetic algorithm

A simulation and optimization model was developed using a genetic algorithm optimization method for decision support in irrigation project planning. The model was applied to an irrigated area in Delta, Utah for optimizing economic benefit, simulating the water demand, and searching the related crop area percentages with specified water supply and planted area constraints. This model can be applied to other irrigation projects and the results are useful for irrigation managers for planning irrigation application depths and for allocating crop areas for achieving optimal economic benefit.; The user interface model begins with the weather generation submodel, which generates daily weather data based on long-term monthly average and standard deviation data. The information provided by the weather generation submodel was applied to the on-farm irrigation scheduling submodel to simulate the daily crop water demand and relative crop yield for seven crops in two command areas. The results from the on-farm irrigation scheduling submodel were used in the genetic algorithm submodel to optimize the project benefit by searching for the best allocation of planted crop areas given the constraints of projected water supply. Two other optimization methods, simulated annealing and iterative improvement, were compared with the genetic algorithm method.; The final results show that both the genetic algorithm and simulated annealing methods can determine the near global optimal benefit with similar values of project water demand and planted crop areas. On the other hand, the iterative improvement method often finds only local optima. The average and standard deviation of strings within one GA population were calculated by the genetic algorithm searching procedure. The results show that the simple genetic algorithm performs very well because the average values increase while the standard deviation decreases from one generation to the next. This study also demonstrates that the genetic algorithm optimization method can be successfully applied in the field of irrigation water management.