[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127058-en":3,"doc-seo-127058-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127058,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity","This paper examines relationships between solar activity and earthquakes, applying machine learning models including k-nearest neighbour, support vector regression, random forest regression, and a long short-term memory network. Experimental data integrate measurements from SILSO, NOAA, GOES, NASA OMNIWeb, and the United States Geological Survey. The dataset includes the 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature, and analyzes earthquake frequency by magnitude and depth. Results indicate the long short-term memory model predicts earthquakes more accurately and that solar activity relates more strongly to lower magnitudes and shallow depth events.","Shkuratskyy, Slav, Usman, Aminu ORCID  \nlogoORCID: [https://orcid.org/0000-0002-4973-3585](https://orcid.org/0000-0002-4973-3585) , O'Dea, Mike ORCID logoORCID: [https://orcid.org/0000-0003-2112-0194](https://orcid.org/0000-0003-2112-0194) , Rehman, Mujeeb Ur ORCID logoORCID: [https://orcid.org/0000-](https://orcid.org/0000-)[ ](https://orcid.org/0000-)[0002-4228-385X and Sabuj](0002-4228-385X and Sabuj) , Saifur Rahman (2024) A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity. International Journal of Computer and Information Engineering, 18 (7) . pp. 380-387.  \nDownloaded from: [https://ray.yorksj.ac.uk/id/eprint/11665/](https://ray.yorksj.ac.uk/id/eprint/11665/)  \nThe version presented here may differ from the published version or version of record. If you intend to cite from the work you are advised to consult the publisher's version: [https://publications.waset.org/10013721/a-machine-learning-approach-for-earthquake](https://publications.waset.org/10013721/a-machine-learning-approach-for-earthquake)prediction-in-various-zones-based-on-solar-activity  \nResearch at York St John (RaY) is an institutional repository. It supports the principles of open access by making the research outputs of the University available in digital form. Copyright of the items stored in RaY reside with the authors and/or other copyright owners. Users may access full text items free of charge, and may download a copy for private study or non-commercial research. For further reuse terms, see licence terms governing individual outputs. Institutional Repository Policy Statement  \nRaY  \nResearch at the University of York St John  \nFor more information please contact RaY at [ray@yorksj.ac.uk](ray@yorksj.ac.uk)  \nWorld Academy of Science, Engineering and Technology  \nInternational Journal of Computer and Information Engineering  \nVol:18, No:7, 2024  \nA Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity  \nViacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Mujeeb Ur Rehman, Saifur Rahman Sabuj  \n[waset.org/10013721.pdf](waset.org/10013721.pdf)  \nScience Index, Computer and Information Engineering Vol: 18, No:7, 2024 publications .  \nOpen  \nAbstract—This paper examines relationships between solar activity and earthquakes, it applied machine learning techniques: Knearest 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  \nKeywords—K-Nearest Neighbour, Support Vector Regression, Random Forest Regression, Long Short-Term Memory Network, earthquakes, solar activity, sunspot number, solar wind, solar flares.  \nI. INTRODUCTION  \nAN earthquake is characterized by a variety of fundamental  \nfactors, such as its depth, hypocentre, and magnitude. The distance between the Earth’s surface and 700 kilometres below the surface is the depth of an earthquake. The hypocentre, which designates the beginning of an earthquake, is located in the shallow (0-70 km), intermediate (70-300 km), and deep (300- 700 km) zones of this subterranean area. The size of an earthquake is determined by its magnitude. For instance, an earthquake of magnitude 5.3 is regar","cbCaiocyq8iTokEt","https://ap.wps.com/l/cbCaiocyq8iTokEt","pdf",932893,1,9,"English","en",105,"# Abstract\n# Keywords\n# I. Introduction","[{\"question\":\"Which machine learning techniques are used for earthquake prediction?\",\"answer\":\"The study uses k-nearest neighbour, support vector regression, random forest regression, and a long short-term memory network.\"},{\"question\":\"What data sources support the experiment?\",\"answer\":\"It integrates data from the SILSO World Data Center, NOAA National Center, GOES satellite, NASA OMNIWeb, and the United States Geological Survey.\"},{\"question\":\"How does solar activity relate to earthquakes according to the findings?\",\"answer\":\"The long short-term memory model predicts more correctly, and solar activity is more likely to affect earthquakes with lower magnitude and shallow depth than larger, deeper events.\"}]","A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity | PDF",1785936589,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-approach-for-earthquake-prediction-in-various-zones-based-on-solar-activity","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-for-earthquake-prediction-in-various-zones-based-on-solar-activity/127058/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning techniques are used for earthquake prediction?","Question",{"text":75,"@type":76},"The study uses k-nearest neighbour, support vector regression, random forest regression, and a long short-term memory network.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources support the experiment?",{"text":80,"@type":76},"It integrates data from the SILSO World Data Center, NOAA National Center, GOES satellite, NASA OMNIWeb, and the United States Geological Survey.",{"name":82,"@type":73,"acceptedAnswer":83},"How does solar activity relate to earthquakes according to the findings?",{"text":84,"@type":76},"The long short-term memory model predicts more correctly, and solar activity is more likely to affect earthquakes with lower magnitude and shallow depth than larger, deeper events.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]