Abstract
Earthquake prediction has been a challenging research area, where a future occurrence of the devastating catastrophe is predicted. In this work, sixty seismic features are computed through employing seismological concepts, such as Gutenberg-Richter law, seismic rate changes, foreshock frequency, seismic energy release, total recurrence time. Further, Maximum Relevance and Minimum Redundancy (mRMR) criteria is applied to extract the relevant features. A Support Vector Regressor (SVR) and Hybrid Neural Network (HNN) based classification system is built to obtain the earthquake predictions. HNN is a step wise combination of three different Neural Networks, supported by Enhanced Particle Swarm Optimization (EPSO), to offer weight optimization at each layer. The newly computed seismic features in combination with SVR-HNN prediction system is applied on Hindukush, Chile and Southern California regions. The obtained numerical results show improved prediction performance for all the considered regions, compared to previous prediction studies.
Highlights
Earthquakes are one of the major catastrophe and their unpredictability causes even more destruction in terms of human life and financial losses
The bright aspect of recent achievements is the encouraging results for earthquake prediction achieved through Computational Intelligence (CI) and Artificial Neural Networks (ANN) in combination with seismic parameters [7,8,9,10,11,12], initiating new lines of research and ideas to explore for earthquake prediction
Predictions are made through an algorithm, it yields four categories of outputs, namely true positive (TP), false positive (FP), true negative (TP) and false negative (FN)
Summary
Earthquakes are one of the major catastrophe and their unpredictability causes even more destruction in terms of human life and financial losses. The bright aspect of recent achievements is the encouraging results for earthquake prediction achieved through Computational Intelligence (CI) and Artificial Neural Networks (ANN) in combination with seismic parameters [7,8,9,10,11,12], initiating new lines of research and ideas to explore for earthquake prediction. Earthquake prediction problem is initially considered as a time series prediction [14] Later, seismic parameters are mathematically calculated on the basis of well-known seismic laws and facts, corresponding to every target earthquake (Et). Earthquake prediction studies based on Artificial Neural Networks (ANN) have been performed on the considered seismic regions [9, 11, 15].
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