Formal verification of tree-based machine learning models for lateral spreading
Machine learning models for geotechnical hazard prediction can achieve high accuracy while learning physically inconsistent relationships from sparse or biased training data. Current remedies (post-hoc explainability, such as SHAP and LIME, and training-time constraints) either diagnose individual predictions approximately or restrict model capacity without providing exhaustive guarantees. This paper encodes trained tree ensembles as logical formulas in a Satisfiability Modulo Theories (SMT) solver and checks physical specifications across the entire input domain, not just sampled points. Four geotechnical specifications (water table depth, PGA monotonicity, distance safety, and flat-grou...
| Authors | Krishna Kumar |
|---|---|
| Primary category | cs.LG |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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