Prediction of earthquake magnitude using adaptive neuro fuzzy inference system

Abstract

The present work emphasizes forecasting the occurrence of earth- quakes using a smart and intelligent tool called adaptive neuro-fuzzy inference system(ANFIS). ANFIS can be considered as the fusion of artificial neural networking and fuzzy inference system, which is the smarter version of predicting tool. For this purpose information regarding forty-five real earthquakes are collected from different regions. During the period of 1933 and 1985, earth- quakes from different stations are assembled having magnitude not less than 5. Thereupon, two algorithms are used to develop a model with ANFIS, which tries to produce a better prediction of earthquake magnitude. The higher mag- nitude of earthquakes leads to devastating the life and economy, hence for the safety of the vicinity, the prediction of earthquake magnitude can be a life- saving approach which is quite challenging. Adopting this approach is a very fast and economic way of prediction. Out of grid partitioning and subtractive clustering, subtractive clustering is found to be matchless in a prediction of earthquake magnitude for the data selected in this research.