Machine Learning Based Algorithm for Earthquake Monitoring

In this paper, we are trying to predict the Earthquake before it happens using seismic indicators anywhere around the world. It is an important task, on detecting seismic events from seismic time series using machine learning techniques. When we train a dataset for any one location it should also be applicable to detect at the other locations. Earthquakes are one of the most dangerous and deadly natural disasters. It is very important for the safety of all human beings as well as all the animals living on the planet to predict the earthquake way before it is going to happen. The population of the earth is increasing at a very rapid rate so the human population is also settling in the areas where the region is at very high risk of earthquakes which also causes more people to be affected by the impact of the earthquakes and more people to die. During this research various machine learning techniques were used such as Support Vector Machine, Random Forest, Neural Network, Convolutional Neural Network, Quantum Machine Learning, K-Nearest Neighbor, Machine Learning Risk Prediction Algorithm, Deep learning Model, and Regression.