Anomalies Detection in Radon Time Series for Earthquake Prediction Using Machine Learning Techniques

Abstract

Radioactive soil and water radon gas emission is a significant precursor to earthquakes.The meteorological parameters such as temperature, pressure, humidity,rainfall, and windspeed influence the radon gas emission from the medium suchas soil and water. In this study, radioactive soil radon gas has been investigatedfor earthquake prediction. Before the seismic events, radon gas emission is also affectedby seismic energies. These seismic energies are responsible for the changesinside the earth�s crust, which causes earthquakes on earth. Our focus in this workis first to predict the radon gas concentration using Machine Learning algorithmsand then identify anomalies before and after the seismic events using standardconfidence interval methods. We experimented with different machine learningmodels for the detailed comparative study of radon concentration predictions. Adataset is divided into different settings of training and testing data. Testing dataincludes the seismic samples only. The models are trained on non-seismic daysamples and some of the seismic day samples and tested on seismic day samples.After acceptable predictions, anomaly detection can be done on test data.A simple mean plus two standard deviations away test has been used to identifythe original measured radon values, which are out of this prediction confidenceinterval. These values are then considered as an anomaly