Building Damage Assessment to Facilitate Post-Earthquake Search and Rescue Missions by Leveraging a Machine Learning Algorithm

Earthquakes have a severe impact on people's lives and infrastructure. Many emergency institutes and search and rescue missions need accurate post-earthquake response strategies, particularly in building damage assessment. Traditional methods, relying on manual inspections, are inefficient compared to Machine Learning (ML) algorithms. Thus, Random Forest (RF) algorithms stand out because they handle diverse datasets effectively and minimize overfitting. The study outlines the methodology encompassing data preparation, exploratory analysis, feature engineering, and model building, employing a preprocessing pipeline integrating numerical and categorical features. Additionally, Principal Component Analysis (PCA) is applied to reduce dimensionality. The results of the RF model showed an accuracy of 94 % and the highest Fl-score of 97% among all the grades, demonstrating its efficacy in predicting damage grades post-earthquake. The results can help support better disaster management plans by helping to prioritize rescue operations and allocate resources wisely.