[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126987-en":3,"doc-seo-126987-105":29,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126987,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity - Journal Article Study Summary","This paper investigates how solar activity relates to earthquake occurrence and evaluates machine learning models for predicting earthquakes across different zones. It uses data from SILSO, NOAA, GOES, NASA OMNIWeb, and the United States Geological Survey, incorporating the 23rd and 24th solar cycles plus sunspot and solar wind variables. Sunspots, solar wind, and solar flares are analyzed together with earthquake frequency distribution by magnitude and depth. Results show that the long short-term memory network predicts earthquakes more accurately than KNN, support vector regression, and random forest regression.","World 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 regarded as moderate, but an  \nearthquake of magnitude 6.3 is regarded as powerful. Earthquakes are caused by a range of natural and artificial reasons, and they often happen along plate tectonic borders. As shown in Fig. 1, there are two types of earthquake impacts: internal and exterior Earth effects. The first kind of earthquake is caused by tectonic activity or by things that happen inside the earth, like rain, volcanoes, or landslides. The second type of earthquake cause is non-tectonic or external earth effects, such as sun and moon gravitation and solar activity.  \nWhile earthquakes occur on the Earth’s surface, solar activity events occur on the Sun’s surface, with a distance of  \nViacheslav Shkuratskyy is with Department of Computer Science, York St John, Lord Mayor’s Walk, York, YO31 7EX, UK, (corresponding author, e[mail: viacheslav.shkurat@yorksj.ac.uk](mail: viacheslav.shkurat@yorksj.ac.uk)).  \nAminu Bello Usman is with School of Computer Science, University of Sunderland, Edinburgh Building, City Campus, Chester Road, Sunderland, SR1 3SD, UK (e-mail: [aminu.usman@sunderland.ac.uk](aminu.usman@sunderland.ac.uk)).  \nMichael O’Dea is with School of Computer Science, University of York, York YO10 5DD, UK ([e-mail: michael.odea@york.ac.uk](e-mail: michael.odea@york.ac.uk)).  \napproximately 1.5 × 1011 m between them [1] . Apparently, there does not appear to be any connection between the sun and earthquakes. Despite this, it still is not known how or how much solar activity affects earthquakes, some studies have shown a link between these two phenomena [2]. Wolf [3] was one of the first to show that these two seemingly unrelated events are linked (earthquake and solar activity) . Wolf’s assertions are supported by other resear","cbCaigFWb9e6y2uu","https://ap.wps.com/l/cbCaigFWb9e6y2uu","pdf",904827,1,"English","en",105,"# Introduction\n## Earthquake fundamentals and impact factors\n## Solar activity events and prior research\n## Motivation and AI/machine learning context\n# Methods and data sources\n# Experimental setup and evaluation\n# Results and discussion","[{\"question\":\"Which solar-related datasets and variables are used to build the earthquake prediction dataset?\",\"answer\":\"The study uses data from SILSO, NOAA, GOES, NASA OMNIWeb, and the United States Geological Survey. It includes the 23rd and 24th solar cycles and variables such as daily sunspot number, solar wind velocity, proton density, and proton temperature, along with sunspot, solar wind, and solar flare indicators.\"},{\"question\":\"Which machine learning models are applied in the paper?\",\"answer\":\"The paper applies K-nearest neighbour, support vector regression, random forest regression, and a long short-term memory network to model earthquake prediction.\"},{\"question\":\"What is the main finding about model performance and the effect of solar activity?\",\"answer\":\"The long short-term memory network predicts earthquakes more correctly than the other models. Solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate and deep depth.\"}]","A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity - Journal Article Study Summary | PDF",1785936057,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"a-machine-learning-approach-for-earthquake-prediction-in-various-zones-based-on-solar-activity-journal-article-study-summary","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/a-machine-learning-approach-for-earthquake-prediction-in-various-zones-based-on-solar-activity-journal-article-study-summary/126987/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which solar-related datasets and variables are used to build the earthquake prediction dataset?","Question",{"text":75,"@type":76},"The study uses data from SILSO, NOAA, GOES, NASA OMNIWeb, and the United States Geological Survey. It includes the 23rd and 24th solar cycles and variables such as daily sunspot number, solar wind velocity, proton density, and proton temperature, along with sunspot, solar wind, and solar flare indicators.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are applied in the paper?",{"text":80,"@type":76},"The paper applies K-nearest neighbour, support vector regression, random forest regression, and a long short-term memory network to model earthquake prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main finding about model performance and the effect of solar activity?",{"text":84,"@type":76},"The long short-term memory network predicts earthquakes more correctly than the other models. Solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate and deep depth.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"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":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":28,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":28,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]