用可解释AI分析流域水文连通性，揭示不同区域功能差异。
Explainable artificial intelligence (XAI) for scaling: An application for deducing hydrologic connectivity at watershed scale
- 结合物理模型与LSTM，用XAI解析输入影响
- 识别出流域内各子区域的水文功能差异
- 为水文组织机制提供定量可解释的指标
可解释人工智能（XAI）方法已被用于解读深度学习模型结果，但将XAI与已有水文知识结合以实现过程理解的应用仍有限。本文展示，点尺度应用的XAI方法可用于跨尺度水文响应聚合这一尺度问题的核心挑战，以水文连通性为例。基于物理机制的水文模型生成土壤湿度及其运移数据，用于训练长短期记忆（LSTM）网络，并通过XAI方法评估输入变量的影响。结果表明，基于XAI的分类能有效识别流域尺度下各子区域的功能角色差异。聚合后的XAI结果可作为水文连通性发展的显式、定量指标，为水文组织机制提供新见解。该框架可推广至其他地物物理响应的聚合，推动过程认知深化。
原文摘要 · Abstract (English)
Explainable artificial intelligence (XAI) methods have been applied to interpret deep learning model results. However, applications that integrate XAI with established hydrologic knowledge for process understanding remain limited. Here we show that XAI method, applied at point-scale, could be used for cross-scale aggregation of hydrologic responses, a fundamental question in scaling problems, using hydrologic connectivity as a demonstration. Soil moisture and its movement generated by physically based hydrologic model were used to train a long short-term memory (LSTM) network, whose impacts of inputs were evaluated by XAI methods. Our results suggest that XAI-based classification can effectively identify the differences in the functional roles of various sub-regions at watershed scale. The aggregated XAI results could be considered as an explicit and quantitative indicator of hydrologic connectivity development, offering insights to hydrological organization. This framework could be used to facilitate aggregation of other geophysical responses to advance process understandings.
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