Global Sensitivity Analysis of Parameters for Irrigation Water Optimization Model and Uncertainty Optimization

被引：0
|
作者：
Jiang Y. [1 ,2 ]
Yan Z. [1 ]
Li L. [1 ,2 ]
Yan F. [1 ,2 ]
Xiong L. [1 ,2 ]
机构：
[1] School of Infrastructure Engineering, Nanchang University, Nanchang
[2] Key Laboratory of Poyang Lake Environment and Resources Utilization, Ministry of Education, Nanchang University, Nanchang
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2023年 / 54卷 / 07期
关键词：
irrigation water; LH; -OAT; optimal allocation; sensitivity analysis; uncertainty;
D O I：
10.6041/j.issn.1000-1298.2023.07.037
中图分类号：
学科分类号：
摘要：
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. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
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页码：372 / 380
页数：8
相关论文
共 27 条
  • [1] CHEN Hongguang, WANG Qiongya, LI Xiaoning, Et al., Optimal water resources planning based on interval-parameter two-stage robust stochastic programming model, Transactions of the Chinese Society for Agricultural Machinery, 50, 3, pp. 271-280, (2019)
  • [2] PROVENCHER B, BURT 0., Approximating the optimal ground water pumping policy in a multiaquifer stochastic conjunctive use setting, Water Resources Research, 30, 3, pp. 833-843, (1994)
  • [3] PAUL S, PANDA S N, KUMAR D N., Optimal irrigation allocation: a multilevel approach, Journal of Irrigation and Drainage Engineering, 126, 3, pp. 149-156, (2000)
  • [4] SINGH A., Optimal allocation of resources for the maximization of net agricultural return, Journal of Irrigation and Drainage Engineering, 138, 9, pp. 830-836, (2012)
  • [5] YANG Na, FU Qiang, LI Rongdong, Et al., Application of ant colony algorithm based continuous space in optimizing irrigation regime of rice[J], Transactions of the CSAE, 26, pp. 134-138, (2010)
  • [6] CHEN Xiaonan, DUAN Chunqing, QIU Lin, Et al., Application of large scale system model based on particle swarm optimization to optimal allocation of water resources in irrigation areas [J], Transactions of the CSAE, 24, 3, pp. 103-106, (2008)
  • [7] KARAMOUZ M, ZAHRAIE B, KERACHIAN R, Et al., Crop pattern and conjunctive use management: a case study, Irrigation and Drainage, 59, 2, pp. 161-173, (2010)
  • [8] LI Mo, JIANG Yao, GUO Ping, Et al., Irrigation water optimal allocation considering stakeholders of different levels [J], Transactions of the Chinese Society for Agricultural Machinery, 48, 5, pp. 199-207, (2017)
  • [9] GUO P, WANG X, ZHU H, Et al., Inexact fuzzy chance-constrained nonlinear programming approach for crop water allocation under precipitation variation and sustainable development, Journal of Water Resources Planning and Management, 140, 9, (2014)
  • [10] MA Hanqing, ZHANG Kun, MA Chunfeng, Et al., Research progress on parameter sensitivity analysis in ecological and hydrological models of remote sensing[J], National Remote Sensing Bulletin, 26, 2, pp. 286-298, (2002)
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