Balanced hydropower and ecological benefits in reservoir-river-lake system: An integrated framework with machine learning and game theory

Shuangjun Liu; Xiang Fu; Yu Li; Xuefeng Chu

doi:10.1016/j.jenvman.2024.123746

Balanced hydropower and ecological benefits in reservoir-river-lake system: An integrated framework with machine learning and game theory

J Environ Manage. 2024 Dec 17:373:123746. doi: 10.1016/j.jenvman.2024.123746. Online ahead of print.

Authors

Shuangjun Liu¹, Xiang Fu², Yu Li¹, Xuefeng Chu³

Affiliations

¹ State Key Laboratory of Water Resources Engineering and Management, Wuhan University, China.
² State Key Laboratory of Water Resources Engineering and Management, Wuhan University, China. Electronic address: xfu@whu.edu.cn.
³ Department of Civil, Construction and Environmental Engineering, North Dakota State University, ND, United States.

PMID: 39693991
DOI: 10.1016/j.jenvman.2024.123746

Abstract

The negative impacts of large hydroelectric reservoirs on downstream ecosystems have attracted worldwide attention. Few attempts have been made to dynamically predict ecological benefits and rationally negotiation in the reservoir-river-lake (RRL) system. This study addresses these gaps by developing an integrated framework with machine learning and game theory to balanced hydropower and ecological benefits. The proposed framework integrated the RRL system simulation with a bargaining model, utilizing a machine learning model to forecast lake levels and the equivalent factor method to assess downstream ecosystem service values (ESV). The study evaluated the framework's generalizability and accuracy by applying random and actual runoff series within the Three Gorges Reservoir to the Dongting Lake region. The 2022 mega-drought case study revealed that the Nash equilibrium operation could simultaneously enhance hydropower generation (7.55%) and ecological benefits (20.00%). Notably, ESV improvements of 61.58% during the post-flood season and 36.07% during the dry season underscored the framework's effectiveness in elevating the ecological benefits. The comparison with traditional multi-objective optimization showed that the proposed framework provided reliable and acceptable solutions for decision-makers. The dynamic weight change elucidates the intricate interactions between economic and ecological benefits, enabling a nuanced adjustment of the single weights in the traditional framework. In addition to enhancing the theoretical framework, the Nash equilibrium solutions also showed positive effects on the carbon cycle, plant growth, and animal habitats in Dongting Lake, further highlighting the practical significance. This research offers a practical management tool for reconciling the conflict in an RRL integrated system, providing theoretical and practical insights into sustainable environment and ecosystem management.

Keywords: Ecological benefit; Game theory; Machine learning; Reservoir operation; Reservoir-river-lake system.