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大型连接河流的湖泊中水文干旱的归因:来自可解释机器学习模型的见解
Chenyang Xue1, Qi Zhang2, Yuxue Jia1
1The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210024, China; Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
波扬湖 (Poyang Lake) 是一个湖泊.
科学领域:
- 水文学和气候科学 气象学和气候科学
- 环境建模环境建模
- 水资源管理水资源的管理.
背景情况:
- 大湖对供水和气候调节至关重要,但受到极端干旱的威胁.
- 了解波阳湖等湖泊的干旱机制对于水资源管理至关重要.
研究的目的:
- 开发一种可解释的机器学习模型,用于模拟和解释湖泊水位变化.
- 为了确定波阳湖的水文干旱趋势和极端事件.
- 要归因于干旱加剧和极端事件的原因.
主要方法:
- 开发了一个贝叶斯优化 (BO) 长短期记忆 (LSTM) 模型,与集成梯度 (IG) 解释方法集成.
- 使用标准化水位指数 (SWI) 和运行理论 (1960-2022) 分析了水文干旱趋势和极端事件.
- 使用的流域和长江流作为BO-LSTM模型的输入特征.
主要成果:
- 波阳湖的水文干旱频率从1960年到2022年增加,特别是在2003年后的秋季.
- 甘河的流入是影响波阳湖水位的主要因素.
- 长江的排水效应加剧了秋季干旱,而低流域的流入导致了极端干旱事件.
结论:
- 开发的可解释的ML方法准确地预测湖泊水位,并提供对干旱归因的见解.
- 甘河的流入和长江的流出大大影响着阳湖的水位和干旱的严重程度.
- 这项研究为了解和管理大湖水文过程提供了一个强大的框架.
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