This paper presents the level of damage prediction to buildings caused by Gorkha Earthquake in Nepal using machine learning techniques. The predictions have been made based on mathematically calculated eight tectonic indicators and past vibrational activity records. In this research the objective is to predict earthquake damage on existing data set of seismic activity by using machine learning techniques. In this study, two well-known approaches of machine learning viz. Neural Network (NN) and Random Forest (RF) have been implemented and optimal parameters for accurate prediction are investigated. The analysis reveals that Random forest method has outperformed the neural network approach for building damage prediction. The F1 score using the random forest classification has been obtained as 74.32%.
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Predicting Damage to Buildings Caused by Earthquakes Using Machine Learning Techniques
Semantic Scholar · Engineering · 2019
Abstract
This paper presents the level of damage prediction to buildings caused by Gorkha Earthquake in Nepal using machine learning techniques. The predictions have been made based on mathematically calculated eight tectonic indicators and past vibrational activity records. In this research the objective is to predict earthquake damage on existing data set of seismic activity by using machine learning techniques. In this study, two well-known approaches of machine learning viz. Neural Network (NN) and Random Forest (RF) have been implemented and optimal parameters for accurate prediction are investigated. The analysis reveals that Random forest method has outperformed the neural network approach for building damage prediction. The F1 score using the random forest classification has been obtained as 74.32%.