Machine Learning Approach for Predicting Earthquakes in a Geographic Location

Abstract: An earthquake is a type of natural disaster that is well-known for the devastation it causes to both naturally existing and artificial structures, including buildings, bungalows, and residential areas, to name a few. Seismometers, which pick up vibrations caused by seismic waves moving through the earth's crust, are used to measure earthquakes. The damage caused by an earthquake was categorised in this work into damage ratings, which have values ranging from one to five. The damage grade of a certain structure, which is linked to a Unique Identification String, was predicted using a previously gathered data set and a number of criteria. An analysis of current machine learning classifier techniques was used to make the forecast. Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier, and K-Nearest Neighbors were the machine learning techniques employed in this study. The best algorithm was taken into consideration after a review of a number of attributes. The method used to predict the property underwent a thorough investigation, and the data analysis that followed revealed information that could help future earthquakes' effects be lessened.

Keywords: Machine learning, Support Vector Machine (SVM), Random Forest Classifier, Logistic Regression, K Nearest Neighbors, and predictive analysis.