Earthquake Damage Risk Classification Using Machine Learning Models Based on Built-Up Area Indices from Satellite Imagery

Abstract

One of the challenges in assessing earthquake damage risk in areas with high seismic activity and limited data is the lack of a detailed building inventory and the absence of available data. Therefore, a remote sensing and machine learning framework is needed that can utilize the built-up area index with NDBI from multitemporal Landsat 8 imagery as a vulnerability proxy, combined with ISGS seismic hazard data, topographic variables from DEM, and surface geology. Support Vector Machine (SVM) and Random Forest (RF) classifiers can be trained and developed to categorize vulnerability classes and evaluate results in terms of accuracy, precision, recall, and F1 score. The SVM achieved an accuracy of 0.970, with precision, recall, and F1 scores all reaching 0.97, while the RF achieved 0.96. This study focuses on Nabire Regency, Central Papua, Indonesia, utilizing various approaches, scaling methods, and a relatively low-cost methodology for the initial screening of earthquake-prone areas without the need for field data collection, with the potential to apply these methods to other seismic regions.