Prediction of seismic zone in india using neural network algorithms

Abstract

In this world, earthquake is a major catastrophe which creates huge amount of loss in living non living things. The prediction of an earthquake is an important task in seismology. Neural network performs a key task in the prediction of earthquake. The neural network architecture are created with different input layer and hidden layers with deep learning optimization algorithms. The input layer was developed with the parameters of historical earthquake data of India taken from India Meteorological Department (IMD). The earthquake event such as date, latitude, longitude, depth, magnitude are mathematically converted into seismic indicators depend on Gutenberg-Richter’s inverse law, are the input layers of this neural network model. The developed network model was trained with set of data items using neural network algorithms such as Backpropagation and sequential learning. The Backpropagation is used to find the magnitude prediction and sequential learning is used to find the prediction model for the cartographic risky areas. The loss and accuracy of the model are analyzed with the help of software tool, Disaster Management System which is developed for this work using Python. The deep neural network optimizers such as Stochastic Gradient Descent (SGD), Adaptive Gradient algorithm (AdaGrad) and Root Mean Square propagation (RMSprop) are used to optimize the prediction model. The optimizer produced earthquake prediction model with high ability and more accuracy. Also give the cartography which shows the seismic zone in India face earthquake in future.