Domain-informed explainable boosting machines for trustworthy lateral spread predictions

Abstract

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\%).

Figures (16)

• Figure 1: Spatial distribution of 2011 Christchurch earthquake showing lateral spreading Yes/No sites.
• Figure 2: Distribution of input features and outliers analysis.
• Figure 3: Relationship between the score $s$ and probability $P(y=1)$ through the logistic function. The classification threshold $P=0.5$ corresponds to $s=0$.
• Figure 4: Round-robin training for univariate shape functions.
• Figure 5: Shape function examples. (a)(b) 1D shape function examples for $f_1$ and $f_2$. The x axis is the feature value and the y axis is the score. (d) 2D shape function example for $f_{1,2}$. The color denotes the score. The red point denotes the example point with feature value $x_1=5$ and $x_2=0.5$.