Data-driven Classification of Tsunami Evacuation Suitability Using XGBoost: A Case Study in Padang City

In this research, we established a machine learning–based model to predict the suitability of tsunami evacuation locations in Padang City through the Extreme Gradient Boosting (XGBoost) method. We trained the model on a new synthetic dataset with 5,000 observations with key geospatial and demographic features such as elevation, distance to coastline, suggested evacuation capacity, surrounding population count and site area. The analysis process consisted of preprocessing, feature selection utilizing the XGBoost Classifier, training and cross-validation on each model, and evaluation through regression as well as classification metrics. The XGBoost model performed best (RMSE=0.0642, MAE=0.0418 and Accuracy=93.8%), which was even better than Random Forest, Gradient Boosting Trees and Logistic Regression models. These findings demonstrate that XGBoost can successfully extract complicated spatial–demographic associations with little overfitting. The residual analysis and the actual-vs-predicted plots also reveal good model calibration and stability. A web prototype was also created to visualize the suitability of evacuation and facilitate spatial decision making. Although the model is based on simulated data, it offers an extendible and interpretable framework to be integrated in practical scenarios with field and operational disaster management systems. To the best of our knowledge, this work represents the first use of XGBoost algorithm in Indonesia to classify tsunami evacuation sites and functions as a new tool for disaster preparedness and evacuation plans on the coast.