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灌区用水优化模型参数全局敏感性分析与不确定性优化
引用本文:姜瑶,颜泽文,黎良辉,闫峰,熊吕阳. 灌区用水优化模型参数全局敏感性分析与不确定性优化[J]. 农业机械学报, 2023, 54(7): 372-380
作者姓名:姜瑶  颜泽文  黎良辉  闫峰  熊吕阳
作者单位:南昌大学工程建设学院,南昌 330031;南昌大学鄱阳湖环境与资源利用教育部重点实验室,南昌 330031;南昌大学工程建设学院,南昌 330031
基金项目:国家自然科学基金项目(51909003、52269012、52209058)
摘    要:灌区水资源优化配置中存在众多不确定性因素,而考虑不确定性因素的优化模型往往存在结构复杂、不确定性参数考虑有限、计算精度和效率较低等问题。本文将LH-OAT(Latin hypercube-One factor at a time)方法与灌区用水优化模型耦合,构建了灌区用水优化模型参数敏感性分析与不确定性优化方法,并以黑河流域中游典型灌区为案例研究区,对模型中6类共25个不确定性参数进行了全局敏感性分析。计算获得了模型中25个不确定性参数的敏感度排序,并从中筛选出10个高敏感性参数,以高敏感性参数作为优化模型不确定性参数输入,获得了不确定性下的灌区用水优化结果。案例分析表明,该方法有效筛选出优化模型中高敏感的关键参数,综合考虑了不确定性参数对模型优化结果的影响,大大减少了模型不确定性参数的表征数量,降低了模型复杂性,有效提高了模型计算效率,可为灌区水资源优化配置问题提供方法参考。

关 键 词:敏感性分析  灌区水资源  优化配置  LH-OAT  不确定性
收稿时间:2022-11-21

Global Sensitivity Analysis of Parameters for Irrigation Water Optimization Model and Uncertainty Optimization
Affiliation:Nanchang University
Abstract:There are many uncertain factors in the optimal allocation of water resources in irrigated areas, while the optimization models considering the uncertainties are often faced with the problems of complex structure, limited uncertain parameters, low calculation accuracy and efficiency. Therefore, a method for parameter sensitivity analysis of irrigation water optimization model as well as uncertainty optimization was developed through coupling the Latin hypercube-One factor at a time (LH-OAT) method with an irrigation water optimization model. Taking a typical irrigation district in the middle reaches of the Heihe River basin as the case study area, the sensitivity analysis method was conducted for 25 uncertainty parameters from six categories parameters of the model, and the uncertainty optimization of irrigation water use was then realized based on the highly sensitive parameters. The sensitivity ranking of 25 uncertainty parameters in the model was calculated, and 10 highly sensitive parameters were selected. Taking the highly sensitive parameters as uncertainty parameters input for the optimization model, the optimized results of irrigation water use under uncertainty were obtained. The case study indicated that the developed method can effectively find the highly sensitive key parameters in the optimization model, and can comprehensively consider the impact of uncertainty parameters on the optimization results. The method can greatly reduce the number of uncertainty parameters to be considered in an optimization model, which reduced the model complexity and effectively improved the efficiency and accuracy of the model. The study can provide important scientific reference and practical methods for the optimal allocation of water resources in irrigated areas.
Keywords:sensitivity analysis  irrigation water  optimal allocation  LH-OAT  uncertainty
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