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<p>Nowadays, water scarcity is getting increasingly severe, particularly in arid and semi-arid areas of China. In addition, there are multiple uncertainties and nonlinear issues in the water resources management process. To address these issues, we developed an improved fuzzy multi-objective nonlinear model incorporating Particle Swarm Optimization and fuzzy programming. The model was applied to a case study in Minqin County, Gansu Province, China, with the objectives of maximizing economic benefit and water productivity. Based on the developed model, a series of irrigation water resources optimal schemes under different <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\alpha {\text{-cut}}\)</EquationSource> </InlineEquation> levels were provided. From the results, as the <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\alpha {\text{-cut}}\)</EquationSource> </InlineEquation> levels increases from 0 to 1, the optimal results for economic benefits, water productivity, and crop irrigation areas show a consistent trend of decreasing upper bounds, increasing lower bounds, and narrowing intervals until a deterministic value occurs at <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\alpha =1\)</EquationSource> </InlineEquation>. Furthermore, the optimal results of the developed multi-objectives model are always in the middle of the optimal results of the individual objectives, which represents that the established model has the ability to deal with multiple conflicting objectives and provide decision-makers with a rang of optimal schemes under uncertainty.</p>

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An Improved Fuzzy Multi-objective Nonlinear Programming Model for Irrigation Water Resources Optimization Allocation Under Uncertainties

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Chongfeng Ren,
  • Linghui Yu,
  • Yu Zhang,
  • Zhishuai Xie

摘要

Nowadays, water scarcity is getting increasingly severe, particularly in arid and semi-arid areas of China. In addition, there are multiple uncertainties and nonlinear issues in the water resources management process. To address these issues, we developed an improved fuzzy multi-objective nonlinear model incorporating Particle Swarm Optimization and fuzzy programming. The model was applied to a case study in Minqin County, Gansu Province, China, with the objectives of maximizing economic benefit and water productivity. Based on the developed model, a series of irrigation water resources optimal schemes under different \(\alpha {\text{-cut}}\) levels were provided. From the results, as the \(\alpha {\text{-cut}}\) levels increases from 0 to 1, the optimal results for economic benefits, water productivity, and crop irrigation areas show a consistent trend of decreasing upper bounds, increasing lower bounds, and narrowing intervals until a deterministic value occurs at \(\alpha =1\) . Furthermore, the optimal results of the developed multi-objectives model are always in the middle of the optimal results of the individual objectives, which represents that the established model has the ability to deal with multiple conflicting objectives and provide decision-makers with a rang of optimal schemes under uncertainty.