Decision support for irrigation project planning using a genetic algorithm

• Published in: Agricultural water management Vol. 45; no. 3; pp. 243 - 266
• Main Authors: Kuo, Sheng-Feng, Merkley, Gary P, Liu, Chen-Wuing
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2000; Elsevier Science; Elsevier
• Series: Agricultural Water Management
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agronomy. Soil science and plant productions; algorithms; Biological and medical sciences; Crop yield; decision making; Decision support; equations; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Genetic algorithm; genetics; Irrigation management; irrigation scheduling; Irrigation. Drainage; Optimization; Simulation; simulation models; Utah; water supply; United States; North America; Utah; America; Irrigation management; Simulation; Genetic algorithm; Crop yield; Optimization; Decision support; Monocotyledones; Zea mays; Hordeum vulgare; Medicago sativa; Model study; Cereal crop; Weather; Leguminosae; Gramineae; Dicotyledones; Angiospermae; Decision aid; Water requirement; Spermatophyta; Yield; Fodder crop; Economic aspect; Simulation model; Triticum aestivum
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/S0378-3774(00)00081-0

This work presents a model based on on-farm irrigation scheduling and the simple genetic algorithm optimization (GA) method for decision support in irrigation project planning. The proposed model is applied to an irrigation project located in Delta, Utah of 394.6 ha in area, for optimizing economic profits, simulating the water demand, crop yields, and estimating the related crop area percentages with specified water supply and planted area constraints. The user-interface model generates daily weather data based on long-term monthly average and standard deviation data. The generated daily weather data are then applied to simulate the daily crop water demand and relative crop yield for seven crops within two command areas. Information on relative crop yield and water demand allows the genetic algorithm to optimize the objective function for maximizing the projected benefits. Optimal planning for the 394.6 ha irrigation project can be summarized as follows: (1) projected profit equals US$ 114,000, (2) projected water demand equals 3.03×10 6 M 3, (3) area percentages of crops within UCA#2 command area are 70.1, 19, and 10.9% for alfalfa, barley, and corn, respectively, and (4) area percentages of crops within UCA#4 command area are 41.5, 38.9, 14.4, and 5.2% for alfalfa, barley, corn, and wheat, respectively. Simulation results also demonstrate that the most appropriate parameters of GA for this study are as follows: (1) number of generations equals 800, (2) population sizes equal 50, (3) probability of crossover equals 0.6, and (4) probability of mutation equals 0.02.