Earthquake loss prediction based on random forest algorithm

Accurate assessment of earthquake damage is of crucial importance for pre-earthquake disaster prevention and mitigation, post-earthquake disaster relief and rapid reconstruction. Most of the existing studies based on actual earthquake damage assessment are limited to a specific region and a certain structure type, and the number of data samples used is also limited. Based on the random forest model, this paper uses 378,037 actual building damage data from the March 11, 2011 Japan Earthquake, and uses the earthquake damage classification issued by the American Applied Technical Council （ATC-13） to predict damage caused by earthquake damage to buildings and to analyze the feature importance of factors affecting building damage. The results show that after using SMOTE method to solve data imbalance and Bayesian approach to optimize of hyperparameters, the accuracy on the test set of the random forest-based prediction model is 68.8%, and the recall rates of the four damage classes are 65.0%, 53.6%, 74.8%, and 81.8%, respectively; the accuracy of the model is further increased to 87.5% by considering the life safety performance to convert the model to dichotomous classification, which greatly improves the existing research applied to construction loss prediction with limited number of data samples and low accuracy of the most severe damage level due to data imbalance. The study of the importance of random forest features showed that the epicenter distance, PGA and v_s30 were the features with the greatest influence on the model output. The earthquake damage assessment model established by this study can achieve rapid and relatively accurate prediction of building damage caused by earthquakes, which is beneficial for the pre-earthquake planning and timely rescue after the earthquake.