用领域知识修正可解释模型，让地震滑坡预测更可信
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
- 基于领域知识修正EBM的特征形状函数
- 在保留数据模式的同时减少非物理预测，准确率下降4-5%
- 适合需要可靠解释的自然灾害预测场景
可解释提升机（EBM）通过加性形状函数提供透明预测，便于直接观察特征贡献。然而，EBM可能学习到非物理关系，降低其在自然灾害应用中的可靠性。本研究提出一种领域信息引导的框架，提升EBM在横向滑移预测中的物理一致性。方法通过领域知识修正学习到的形状函数，在保持数据驱动模式的同时纠正非物理行为。将该方法应用于2011年克赖斯特彻奇地震数据集，成功修正了原始EBM中发现的非物理趋势。最终模型生成更具物理一致性的全局与局部解释，准确率仅下降4–5%，实现可信赖预测的合理权衡。
原文摘要 · Abstract (English)
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\%).
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