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基于物理信息的机器学习框架的月度流量模拟的评估:中间变量的影响在其构建中的影响
Chao Deng1, Peiyuan Sun1, Xin Yin2
1The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Journal of environmental management
|June 3, 2024
概括
基于物理的机器学习 (PIML) 模型增强了水文预测. 这项研究发现,PIML模型的表现优于独立机器学习,PIML-XM显示出一致的优势和中间变量,显著影响了流失模拟.
科学领域:
- 水文和水资源水文与水资源
- 环境建模环境建模
- 机器学习应用 机器学习应用
背景情况:
- 准确的水文预测对于水资源管理至关重要,特别是随着洪水和干旱等极端天气事件的增加.
- 基于物理的机器学习 (PIML) 模型整合了概念水文和机器学习 (ML) 模型,但中间变量的作用尚不清楚.
研究的目的:
- 使用各种水文模型和中间变量构建和比较PIML模型.
- 分析中介变量对PIML模型在各种采集区特征中的表现的影响.
主要方法:
- 在205个CAMELS盆地中为PIML建设选择了一个最佳的ML模型 (Lasso).
- 开发了PIML模型,使用五个月度水平衡模型 (TM,XM,MEP,SLM,TVGM).
- 设计了两个实验设置:S1 (实际蒸发) 和S2 (土壤水分) 作为中间变量.
主要成果:
- 所有五种PIML模型的性能总体上都超过了最佳独立ML (Lasso) 模型.
- 根据PIML-XM模型,在所有盆地中,无论中间变量如何,都显示出一致的优异性能.
- 实际蒸发透气 (S1) 通常比土壤水分 (S2) 性能更好,而S2对干旱度,森林分数和流域面积更敏感.
结论:
- 与独立的ML模型相比,PIML模型在每月流失模拟中提供了显著的改进.
- 中间变量的选择对水文预测中的PIML模型性能产生了重大影响.
- 了解中间变量和采集区特征之间的相互作用是优化PIML模型开发的关键.
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