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通过使用机器学习技术,对连锁大引发的兰-梅康河水文变化的综合评估
Wickramaarachchi C Achini Ishankha1, Sangam Shrestha1, Doan Van Binh2
1Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O.Box 4 Klong Luang, Pathum Thani, 12120, Thailand.
Journal of environmental management
|November 12, 2024
概括
水力发电大显著改变了河流的流动. 这项研究使用机器学习来隔离兰-公河流域的水影响,揭示了大量的水文变化,特别是在水的下游.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 水资源管理 水资源管理
背景情况:
- 级联式水电大在全球范围内越来越多地改变了河流流域的流动模式.
- 兰-公河流域 (LMRB) 面临着巨大的水文变化,由于大规模的水开发.
- 以前的研究经常将气候变化和人类影响结合起来,使得对大引起的变化的评估变得复杂.
研究的目的:
- 量化LMRB的水力变化,特别是由级水电大造成的水力变化.
- 从其他环境因素中分离人类引起的水文影响.
- 为了提供一个更精确的评估工具,对大引起的水文变化.
主要方法:
- 整合水文变化指标 (IHA) 方法与随机森林 (RF) 机器学习 (ML) 进行流量预测.
- 使用基于降水和温度数据的射频模型预测非调节 (自然) 流量.
- 在四个关键站点对冲击前,冲击后和冲击过渡期的水文数据进行分析.
主要成果:
- 观察到显著的水文变化,最明显的是在桑 (77.3%) 和下游下降.
- 所有车站的水文变化从过渡到冲击后的时期增加.
- 与自然条件相比,月均流量,年度极端条件和流量变化动态都发生了很大的变化.
结论:
- 人类活动,主要是水力发电开发,被确定为LMRB中流量变化的主要驱动因素.
- 开发的方法提供了一个更准确的方法来评估大引起的水文变化.
- 这种精确的评估有助于通过隔离人类影响,有效地管理水电.
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