Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can learn non-physical relationships that reduce their reliability in natural hazard applications. This study presents a domain-informed framework to improve the physical consistency of EBMs for lateral spreading prediction. Our approach modifies learned shape functions based on domain knowledge. These modifications correct non-physical behavior while maintaining data-driven patterns. We apply the method to the 2011 Christchurch earthquake dataset and correct non-physical trends observed in the original EBM. The resulting model produces more physically consistent global and local explanations, with an acceptable tradeoff in accuracy (4--5\%).
@article{arxiv.2603.17175,
title = {Domain-informed explainable boosting machines for trustworthy lateral spread predictions},
author = {Cheng-Hsi Hsiao and Krishna Kumar and Ellen M. Rathje},
journal= {arXiv preprint arXiv:2603.17175},
year = {2026}
}