Towards interpreting machine learning models for predicting soil moisture droughts

Determination of the dominant factors which affect soil moisture (SM) predictions for drought analysis is an essential step to assess the reliability of the prediction results. However, artificial intelligence (AI) based drought modelling only provides prediction results without the physical interpretation of the models. Here, we propose an explainable AI (XAI) framework to reveal the modelling of SM drought events. Random forest based site-specific SM prediction models were developed using the data from 30 sites, covering 8 vegetation types. The unity of multiply XAI tools was applied to interpret the site-models both globally (generally) and locally. Globally, the models were interpreted using two methods: permutation importance and accumulated local effect (ALE). On the other hand, for each drought event, the models were interpreted locally via Shapley additive explanations (SHAP), local interpretable model-agnostic explanation (LIME) and individual conditional expectation (ICE) methods. Globally, the dominant features for SM predictions were identified as soil temperature, atmospheric aridity, time variables and latent heat flux. But through local interpretations of the drought events, SM showed a greater reliance on soil temperature, atmospheric aridity and latent heat flux at grass sites, with higher correlation to the time-dependent parameters at the sites located in forests. The temporal variation of the feature which effects the drought events was also demonstrated. The interpretation could shed light on how predictions are made and could promote the application of AI techniques in drought prediction, which may be useful for irrigation and water resource management.