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-# xai-classification
-eXplainable AI for eXtreme Gradient Boosting classification of lateral spreading datasets
+# XAI - Lateral Spreading
+This project investigates the application of eXplainable AI (XAI) techniques on predictive machine learning models for lateral spreading phenomena. We have developed multiple XGBoost models using a dataset sourced from [Durante and Rathje (2022)](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-2998v2). The repository provides resources for data preprocessing, model training, and interpretation using SHAP (SHapley Additive exPlanations) explainers.
+
+## Folder Structure
+**`data` Folder**: Contains both the original and processed datasets. The original dataset, derived from [Durante and Rathje (2021)](https://doi.org/10.1177/87552930211004613), comprises 6,500 datapoints from Christchurch, New Zealand, pertaining to the 2011 Christchurch Earthquake. It includes various features such as geometry features, event-specific features like groundwater depth (GWD) and peak ground acceleration (PGA), CPT (cone penetration test) related features, and binary indicators for lateral spreading. Refer to Table 1 for a breakdown of features used in each model.
+
+
+**Table 1.** Summary of features used in each XGBoost model.
+|Model|L<br>(km)|GWD<br>(m)|PGA<br>(g)|Elevation<br>(m)|Slope<br>(%)|I<sub>c</sub><br>(med)|I<sub>c</sub><br>(std)|q<sub>c1Ncs</sub><br>(med)|q<sub>c1Ncs</sub><br>(std)|
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+|A|✓|✓|✓|✓|✓|O|O|O|O|
+|B|✓|✓|✓|✓|✓|✓|✓|✓|✓|
+|C|✓|✓|✓|✓|O|✓|✓|O|O|
+<br>
+
+**`model_development` Folder**: Includes Jupyter notebooks for data preprocessing (_`data_preprocessing.ipynb`_) and XGBoost model training (_`xgb_training.ipynb`_). The data preprocessing notebook loads the dataset, performs data splitting, and feature selection according to Table 1, saving the processed data as pickle files (_`data_x.pkl`_) in **`data`** folder. The XGBoost training notebook demonstrates the model training process and saves the trained models as pickle files (_`opt_XGB_X.pkl`_) in the **`xgb_models`** folder.
+<br>
+
+**`model_usage` Folder**: Contains Jupyter notebooks (_`shap_explainer_X.ipynb`_) for generating SHAP explanations for each XGBoost model. These notebooks load the trained models and corresponding data to create SHAP visualizations.
+<br>
+
+**`xgb_models` Folder**: Stores the trained XGBoost models developed from different datasets in the **`data`** folder.
+
+## References
+Durante, M. G. and Rathje, E. (2022). Machine learning models for the evaluation of the lateral spreading hazard in the Avon river area following the 2011 Christchurch earthquake. doi:10.17603/DS2-3ZDJ-4937
+
+Durante, M. G. and Rathje, E. M. (2021). An exploration of the use of machine learning to predict lateral spreading. Earthquake Spectra 37, 2288–2314. doi:10.1177/87552930211004613
+
+## Citation