Beyond prediction: An integrated post-hoc approach to interpret complex model in hydrometeorology

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With the increasing application of machine learning (ML) in hydrometeorology, we face the urge to demystify the ML's black-box nature because it usually does not provide physically interpretable information to users. Here, we demonstrate multiple post-hoc interpretation methods to evaluate feature effects for hydrometeorological prediction. These methods were integrated and applied to soil moisture (SM) as an example, and random forest was used to establish a prediction model based on a FLUXNET site in Haibei, China. Different views of interpretability, feature importance, Shapley values, partial dependence plot, individual conditional expectation, and accumulated local effect were used to investigate how features affect the prediction. The result shows that a comprehensive understanding can be achieved of the relationship between predicted SM and affecting variables, including lagged SM, precipitation, and soil temperature. Thus, we advocated integrated interpretation tools to enhance the practicability of ML for hydrologists and other physical scientists. A toolbox named ExplainAI is provided.