地下水深对影响因素的歇斯底里反应使用可解释的学习模型框架与沙普利值
Xinyi Niu1, Chengpeng Lu2, Ying Zhang3
1College of Hydrology and Water Resources, Hohai University, Nanjing 210098, Jiangsu, China.
The Science of the total environment
|September 1, 2023
概括
地下水的深度对气候和人类活动的反应有所滞后,人口和GDP等人类因素对深层地下水的影响更大. 机器学习模型表现出良好的表现,但与突然变化作斗争.
科学领域:
- 水文学的水文学
- 环境科学 环境科学
- 机器学习应用 机器学习应用
背景情况:
- 地下水深度预测通常使用机器学习,但对各种因素的歇斯底里反应未得到充分研究.
- 了解气候,人类活动和地下水动态之间的相互作用对于可持续的水资源管理至关重要.
研究的目的:
- 研究地下水深度对气候和人为因素的歇斯底里反应.
- 分析输入变量与地下水深度之间的滞后相关性和时间.
- 用可解释性方法解释机器学习模型预测.
主要方法:
- 选择了六个因素:降雨量 (P),风速 (WS),温度 (T),人口 (POP),国内生产总值 (GDP) 和有效灌面积 (EIA).
- 使用滞后相关系数和滞后时间分析歇斯底里.
- 采用支持矢量机 (SVM) 模型进行地下水深度预测,并采用沙普利增量解释 (SHAP) 方法进行解释.
主要成果:
- 人类变量与地下水深度的相关性比气候变量更强.
- 滞后时间通常在四个月以下,除了深地下水中的EIA和WS外.
- SVM模型实现了89%的令人满意的性能,但在地下水深度的突然变化或显著季节性变化方面遇到了困难.
结论:
- 人类活动,特别是人口和国内生产总值,对灌地区的地下深水产生重大影响.
- 在城市环境中,人类活动主导浅地下水,而气候影响在郊区增加.
- 该研究提出了一种基于多因素的概念模型,用于分析地下水动态.
相关概念视频
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01:26Regulation of Water Output
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01:18Uniform Depth Channel Flow: Problem Solving
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