Document Type : Primary Research paper
Associate Professor, Dept. of Electrical and Electronics Engineering, R. R. Institute of Technology, Bangalore, Visvesvaraya Technological University, Karnataka.
Assistant Professor, Dept. of Information Science, R. R. Institute of Technology, Bangalore Visvesvaraya Technological University, Karnataka, India.
The research based on Seismology gives the importance around the globe new tools methods, new tools and algorithms are needed in order to predict the magnitude, time and geographic location, and also to analyse the effects of earthquakes and in future to safeguard the human lives. Due to the highly random nature of the earthquakes gives the highly random nature of the earthquakes and the complexity in obtaining an efficient mathematical model, until now the efforts are insufficient and new methods are capable of contributing to this challenge is needed. In this present work a novel method based on earthquakes magnitude prediction method is proposed based on the measurements of more than two decades of seismology events which is modeled using machine learning. Richter magnitude (ML) of 5.3 and a depth of 299 km in the study region, located at 14ο-37 ο N and 68 ο -95 ο E, was used as a training data to construct an initial earthquake Richter magnitude (ML) prediction back propagation neural network model with two hidden layers. By using final weights and biases, the back propagation neural network model is implemented. It will be analysed for an embedded earthquake Richter magnitude (ML) prediction back propagation neural network (EEMPBPNN).