Seismic Intensity Estimation for Earthquake Early Warning Using Optimized Machine Learning Model

Abstract

The need for an earthquake early-warning system (EEWS) is unavoidable in order to save lives. In terms of managing earthquake disasters and achieving effective risk mitigation, the quick identification of the earthquake’s intensity is a valuable factor. In light of this, the on-site intensity measurement can be transmitted over an Internet of Things (IoT) network. In this regard, a machine learning (ML) strategy based on numerous linear and non-linear models is proposed in this study for a quick determination of earthquake intensity after two seconds from the P-wave onset. We call this model an on-site two-second ML model-based earthquake intensity determination (2S-ML-EIOS). The utilized dataset INSTANCE for this model is observed by the number of 386 stations from the Italian national seismic network. Our model has been trained on 50,000 occurrences (150 thousand of 2s-three-component seismic windows). The model has the ability to deal with limited features of the waveform traces leading to reliable estimation of the earthquake intensity. The suggested model has a 98.59% accuracy rate in predicting earthquake intensity. The suggested 2S-ML-EIOS model can be used with a centralized IoT system to promptly send the alarm, and the IoT system will then instruct the affected administration to take the appropriate action. The 2S-ML-EIOS results are contrasted with those from the traditional manual solution approach, which corresponds to the ideal solution mean. Based on the extreme gradient boosting (XGB) model, the 2S-ML-EIOS can achieve the best intensity determination, and this improved performance demonstrates the methodology’s efficacy for EEWS.