在水库-河-湖系统中平衡水力发电和生态效益:一个集成的框架与机器学习和游戏理论
Shuangjun Liu1, Xiang Fu1, Yu Li1
1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, China.
Journal of environmental management
|December 18, 2024
概括
本研究引入了一种使用机器学习和游戏理论的新框架,以平衡水电和水库-河流-湖泊系统中的生态效益. 这种方法成功地提高了能源生产和生态系统服务,即使在干旱条件下.
科学领域:
- 环境科学 环境科学
- 生态系统管理 生态系统管理
- 水资源工程 水资源工程
背景情况:
- 大型水力发电水库对下游生态系统产生重大影响,需要更好的管理策略.
- 生态效益的动态预测和水库-河流-湖泊 (RRL) 系统的谈判仍然具有挑战性.
- 现有的方法难以平衡竞争的水电和生态需求.
研究的目的:
- 开发一个综合框架,结合机器学习和游戏理论,以平衡RRL系统中的水电和生态效益.
- 动态预测生态效益,促进RRL系统内的合理谈判.
- 为RRL综合系统的可持续管理提供实用工具.
主要方法:
- 集成的RRL系统模拟与讨价还价模型.
- 利用机器学习进行湖泊水位预测.
- 采用同等因素方法来评估下游生态系统服务价值 (ESV).
- 应用纳什平衡分析来优化操作.
主要成果:
- 在2022年超级干旱案例研究中,纳什平衡操作提高了水电发电7.55%,生态效益增加了20.00%.
- 证明了显著的ESV改进:61.58%的洪水后季节和36.07%的干旱季节.
- 该框架提供了可靠的解决方案,优于传统的多目标优化方法.
结论:
- 开发的框架有效地平衡了RRL系统中竞争的水电和生态效益.
- 动态权重调整为经济-生态权衡提供了细微的见解.
- 这种方法对碳循环,植物生长和动物息地产生了积极的影响,这对于可持续的生态系统管理具有实际意义.
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