Bi-level Multi-objective Complex Regional Water Resources Optimal Allocation Model Under Hybrid Uncertainty

Tao Wang, Jingjing Duan, Chenhui Jiang, Zhaohan Zhang, Dangxian Wang, Xing Li, Ziyu Guan, Jing Zhao, Jiaqi Zhai, Jingzhe Liu, Yulong Gao and Peiling Wang ()
Additional contact information
Tao Wang: Tsinghua University
Jingjing Duan: China Institute of Water Resources and Hydropower Research
Chenhui Jiang: General Institute of Water Resources and Hydropower Planning and Design, Ministry of Water Resources of China
Zhaohan Zhang: North China University of Water Resources and Electric Power
Dangxian Wang: General Institute of Water Resources and Hydropower Planning and Design, Ministry of Water Resources of China
Xing Li: China Institute of Water Resources and Hydropower Research
Ziyu Guan: China Institute of Water Resources and Hydropower Research
Jing Zhao: North China University of Water Resources and Electric Power
Jiaqi Zhai: China Institute of Water Resources and Hydropower Research
Jingzhe Liu: China Institute of Water Resources and Hydropower Research
Yulong Gao: Guangzhou Maritime University
Peiling Wang: Tianjin University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 13, No 15, 7023-7058

Abstract: Abstract To address the coordinated balance between ecological protection and economic benefits in water resources allocation under complex uncertain environments, this paper employs fuzzy random theory to quantify the uncertainty characteristics of the water resources system. Radial Basis Function Neural Networks (RBFNN) are introduced to characterize the nonlinear response relationship of water use benefits, and a bi-level multi-objective optimal allocation model for complex regional water resources systems is established. The model takes the objectives of the watershed water resources system manager as the core task, incorporating the responses of subregion managers to the optimization problem as constraints. The upper level, led by the watershed manager, comprehensively considers environmental and sustainable development goals while balancing water supply and demand for various users. The lower level, dominated by subregion managers, prioritizes resource allocation to high-benefit water use sectors. In terms of solution methodology, the bi-level model is transformed into an equivalent single-layer mixed-integer linear programming problem using the Karush–Kuhn–Tucker (KKT) optimality conditions for simplified computation. The Weihe River Basin is selected as the study area, and stochastic programming is applied to derive coordinated reservoir group scheduling and regional water resources allocation schemes under the average inflow scenario over multiple years. The results validate the significant effectiveness of the allocation scheme in reducing water shortage risks during dry periods and enhancing economic benefits. The findings demonstrate that the model provides an adaptive and robust decision-making approach for water resources conflict management under uncertain scenarios, while also offering technical reference for the subsequent operation of the Han-to-Wei Water Diversion Project. Highlight • Use fuzzy mathematics theory to describe the uncertainty in the water system • Using machine learning to describe economic benefits that are difficult to quantify • Construct a Bi-level multi-objective water resources optimal allocation model. • Apply model to the Shaanxi section of the Weihe River Basin.

Keywords: Water resources allocation; Hybrid uncertainty; Bi-level optimization; KKT; Machine learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-025-04282-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:13:d:10.1007_s11269-025-04282-8

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-025-04282-8

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().