ArticlePloS one2018
Earthquake prediction model using support vector regressor and hybrid neural networks.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Investigating the predictive power of seismic statistical features using ensemble learning.PloS one · 2026Article
- Improving earthquake prediction accuracy in Los Angeles with machine learning.Scientific reports · 2024Article
- Earthquake alerting based on spatial geodetic data by spatiotemporal information transformation learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
4 authors.
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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.
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