Domain-informed explainable boosting machines for trustworthy lateral spread predictions
arXiv cs.LG / 3/19/2026
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Key Points
- The study addresses the tendency of Explainable Boosting Machines (EBMs) to learn non-physical relationships in natural hazard applications and proposes a domain-informed framework to improve physical consistency for lateral spreading prediction.
- The approach modifies learned shape functions using domain knowledge, correcting non-physical behavior while preserving data-driven patterns.
- The method is demonstrated on the 2011 Christchurch earthquake dataset, correcting non-physical trends observed in the original EBM.
- The resulting model yields more physically consistent global and local explanations, with an acceptable tradeoff in accuracy of about 4-5%.
- By enhancing interpretability and physical realism, the work aims to increase the trustworthiness of hazard predictions and the explainability of the model's decisions.
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