A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity

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Earthquake Prediction in Various Zones Based on Solar Activity</title> <meta name="description" content="A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity"> <meta name="keywords" content="K-Nearest Neighbour, Support Vector Regression, Random Forest Regression, Long Short-Term Memory Network, earthquakes, solar activity, sunspot number, solar wind, solar flares."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity"> <meta name="citation_author" content="Viacheslav Shkuratskyy"> <meta name="citation_author" content="Aminu Bello Usman"> <meta name="citation_author" content="Michael O’Dea"> <meta name="citation_author" content="Mujeeb Ur Rehman"> <meta name="citation_author" content="Saifur Rahman Sabuj"> <meta 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mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Viacheslav%20Shkuratskyy">Viacheslav Shkuratskyy</a>, <a href="https://publications.waset.org/search?q=Aminu%20Bello%20Usman"> Aminu Bello Usman</a>, <a href="https://publications.waset.org/search?q=Michael%20O%E2%80%99Dea"> Michael O’Dea</a>, <a href="https://publications.waset.org/search?q=Mujeeb%20Ur%20Rehman"> Mujeeb Ur Rehman</a>, <a href="https://publications.waset.org/search?q=Saifur%20Rahman%20Sabuj"> Saifur Rahman Sabuj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper examines relationships between solar activity and earthquakes, it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity, and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to effect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth</p>. <iframe src="https://publications.waset.org/10013721.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=K-Nearest%20Neighbour" title="K-Nearest Neighbour">K-Nearest Neighbour</a>, <a href="https://publications.waset.org/search?q=Support%20Vector%20Regression" title=" Support Vector Regression"> Support Vector Regression</a>, <a href="https://publications.waset.org/search?q=Random%20Forest%20Regression" title=" Random Forest Regression"> Random Forest Regression</a>, <a href="https://publications.waset.org/search?q=Long%20Short-Term%20Memory%20Network" title=" Long Short-Term Memory Network"> Long Short-Term Memory Network</a>, <a href="https://publications.waset.org/search?q=earthquakes" title=" earthquakes"> earthquakes</a>, <a href="https://publications.waset.org/search?q=solar%20activity" title=" solar activity"> solar activity</a>, <a href="https://publications.waset.org/search?q=sunspot%20number" title=" sunspot number"> sunspot number</a>, <a href="https://publications.waset.org/search?q=solar%20wind" title=" solar wind"> solar wind</a>, <a href="https://publications.waset.org/search?q=solar%20flares." title=" solar flares."> solar flares.</a> </p> <a href="https://publications.waset.org/10013721/a-machine-learning-approach-for-earthquake-prediction-in-various-zones-based-on-solar-activity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013721/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013721/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013721/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013721/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013721/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013721/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013721/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013721/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013721/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013721/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013721.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] N. 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A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity