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Abstract
Earthquake prediction is a branch of the science of seismology concerned with the specification of the time, location, and magnitude of future earthquakes within stated limits and particularly the determination of parameters for the next strong earthquake to occur in a region.Prediction can be further distinguished from earthquake warning systems, which upon detection of an earthquake, provide a real-time warning of seconds to neighboring regions that might be affected.Early Response Warning System is currently responsible for alerting airports,trains, fire stations, etc. coal mine workers are exposed to alife-threatening danger in a form of seismic events. The existing earthquake prediction algorithm based on mathematical analysis, machine learningalgorithms like decision trees and support vector machines, and precursors signal. The existing system do not have very accuracy due to the seemingly dynamic and unpredictable nature of earthquakes. In this proposed systemdeep learning technique called long short-term memory with gradient descent optimization algorithms(LSTMAdaGrad) networks predicting future earthquakes using data of past earthquakes using Long Short-Term Memory neural networks algorithm. The experimental result shows the better performance compare with existing algorithm like spatial temporal analysis.