Major Earthquake Event Prediction Using Various Machine Learning Algorithms

Earthquake reporting system based on social networking site using machine Learning

Abstract:-

An important characteristic of Twitter is its real-time nature. We analyze the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we propose a probabilistic spatiotemporal model for the target event that can find the centre of the event location. We regard each Twitter user as a sensor and apply particle filtering, which is widely used for location estimation. The particle filter works better than other comparable methods for estimating the locations of target events. We develop an earthquake reporting system for use in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (93 percent of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and notification is delivered much faster than JMA broadcast announcements. To propose an algorithm to detect a target event to do semantic analysis on Tweets to obtain tweets on the target events precisely regard Twitter user as a sensor to detect the target event toestimate location of the target.