Earthquake time prediction using catboost and SVR

Abstract

Seismic tremors everywhere throughout the globe have been a noteworthy reason for decimation and death toll and property. The following context expects to recognize earthquakes at a beginning time utilizing AI. This will help individuals and salvage groups to make their errand simpler. The information in this manner comprises of these seismic acoustic signals and the time of failure. The model is then prepared utilizing the CatBoost model and the utilization of Support Vector Machines. This will help foresee the time at which a Seismic tremor may happen. CatBoost Regression Algorithm gives a Mean Absolute Error of about 1.860. The Cross Validation (CV) Score for the Support Vector Machine (SVM) approach is-2.1651. The datasets metrics are not reliable on any outer parameter in this manner the variety of exactness is constrained, and high accuracy is accomplished.