Efficient and equitable irrigation management: A fuzzy multi-objective optimization model integrating water movement processes

This study presents a robust multi-objective model for optimizing irrigation water allocation to balance economic benefits, water equity and irrigation efficiency in irrigation decisions under uncertainty. Daily water movement among irrigation, soil, groundwater, and drainage is modeled through water balance simulation and integrated into an optimization model considering economic, social, and resource objectives. To effectively manage conflicts arising from multiple objectives under uncertainty, interval multi-objective programming and fuzzyboundary interval programming are employed. Here, interactive algorithms are used to align constraint feasibility with objective satisfaction to generate optimal solutions for irrigation water allocation. The model's applicability is demonstrated within the Hetao Irrigation District, addressing the rising conflicts between irrigation water supply and demand. The optimization model aims to maximize net economic benefits, minimize Gini coefficient and the proportion of blue water utilization in irrigation water, incorporating daily soil water and groundwater movement processes. By setting five feasibility levels, optimal water allocation solutions are derived for five irrigation subareas and three crops across their entire growth periods. The results analysis shows that an increased feasibility level makes the uncertainty range of the objective values decrease while lower feasibility levels lead to higher satisfaction with the objective values. Moreover, it is found that the decision satisfaction peaks at a feasibility level of 0.9 after balancing feasibility and objective satisfaction, aligning more closely with the decision maker's expectations. At this level, the economic benefit is [3.62, 13.60] x 109 Yuan. Compared to a feasibility level of 1.0, the average range of the economic benefits increases by 122.50 x 106 Yuan, and the Gini coefficient decreases from [0.7973, 0.7997] to [0.7963, 0.7996]. Therefore, the above results can facilitate decision-making for irrigation optimization in the irrigation area under multiple conflicting objectives and uncertainties, promoting sustainable development of irrigated agriculture across economic, social, and resource dimensions.