[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126831-en":3,"doc-seo-126831-105":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},126831,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity - Abstract","This paper examines relationships between solar activity and earthquakes by applying machine learning models including K-nearest neighbour, support vector regression, random forest regression, and a long short-term memory network. The dataset combines variables such as daily sunspot number, solar wind velocity, proton density, and proton temperature across solar cycles 23 and 24 using data from major astronomical and geophysical sources. Results indicate that the long short-term memory network predicts earthquakes more accurately, with solar activity more strongly associated with lower magnitude and shallow depth events than with magnitude 5.5+ events at greater depth.","Sh kur a st ky, Viach e slav, U sm an , Amin u Bello, O 'De a , Mich a el, Rehm an , Muje eb Ur an d S abuj, S aifar Rahm an (2 0 2 4) AM a chin e Learnin g Appro ach for E arthqu ake Pr e diction in Variou s Zone s Ba se d on Solar Activity. Intern ation al Journ al of Comput er an d Inform ation En gin e ering , 1 8 ( 7) . ISSN 2 0 1 0-3 9 2 1  \nDownloa d e d from : [http :// sur e . sun d erl an d. ac . uk/id/ e print / 1 7 9 0 4 /](http :// sur e . sun d erl an d. ac . uk/id/ e print / 1 7 9 0 4 /)  \nU s a g e g uid eli n e s  \nPle a s e r efer to th e u s a g e guid eline s at [http :// sur e . sun d erl an d . ac. uk/ policies. html](http :// sur e . sun d erl an d . ac. uk/ policies. html) or altern atively cont act [sur e @ sun d erlan d. ac . uk](sur e @ sun d erlan d. 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 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 Comput","cbCaiuKenxCJcySF","https://ap.wps.com/l/cbCaiuKenxCJcySF","pdf",937586,1,10,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"Which machine learning methods are used for earthquake prediction in the study?\",\"answer\":\"The study applies K-nearest neighbour, support vector regression, random forest regression, and a long short-term memory network.\"},{\"question\":\"What solar and earthquake-related variables are included in the dataset?\",\"answer\":\"The dataset includes daily sunspot number, solar wind velocity, proton density, and proton temperature, along with earthquake frequency distribution by magnitude and depth, covering solar cycles 23 and 24.\"},{\"question\":\"What conclusion does the paper reach about which model performs best?\",\"answer\":\"The long short-term memory network predicts earthquakes more correctly than the other models considered.\"}]","A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity - Abstract | PDF",1785935090,25,{"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-abstract","",{"@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-abstract/126831/",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 methods are used for earthquake prediction in the study?","Question",{"text":75,"@type":76},"The study applies K-nearest neighbour, support vector regression, random forest regression, and a long short-term memory network.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What solar and earthquake-related variables are included in the dataset?",{"text":80,"@type":76},"The dataset includes daily sunspot number, solar wind velocity, proton density, and proton temperature, along with earthquake frequency distribution by magnitude and depth, covering solar cycles 23 and 24.",{"name":82,"@type":73,"acceptedAnswer":83},"What conclusion does the paper reach about which model performs best?",{"text":84,"@type":76},"The long short-term memory network predicts earthquakes more correctly than the other models considered.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]