eXplainable AI models for Predicting Lateral Spreading in the 2011 Christchurch Earthquake: SHapley Additive exPlanations approach

This project centers on using eXplainable AI (XAI) techniques, notably SHAP (SHapley Additive exPlanations), to interpret machine learning models forecasting lateral spreading during the 2011 Christchurch Earthquake. The data originates from published data in DesignSafe (PRJ-2998). Through this project, Jupyter notebooks are provided, offering reusable tools for natural hazard studies. Its distinctive approach integrates ML modeling with XAI techniques, making it relevant to researchers, engineers, and data scientists seeking interpretability in AI and natural hazard research.