[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125534-en":3,"doc-seo-125534-105":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":20,"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},125534,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","The Application of Machine Learning for Predicting Global Seismicity - Abstract","Earthquakes are among the deadliest natural disasters, and forecasting them is difficult because tectonic motion, volcanic activity, rainfall, tidal stress, and even human activities can all contribute. Researchers also consider solar activity as a potential driver. This chapter evaluates machine learning approaches—k-nearest neighbour, support vector regression, random forest regression, and LSTM—to predict earthquakes and test relationships with solar phenomena, using sunspots, solar wind, solar flares, and global earthquake frequency by magnitude and depth.","The Application of Machine Learning for Predicting Global Seismicity\nShkuratskyy Viacheslav1, Aminu Bello Usman1, and Michael ODea1\nDepartment of Computer & Data Science1\nYork St John University, UK\n\u0013 HYPERLINK \"mailto:viacheslav.shkurat@yorksj.ac.uk\" \u0014viacheslav.shkurat@yorksj.ac.uk\u0015,\u0013 HYPERLINK \"mailto:a.usman@yorksj.ac.uk\" \u0014a.usman@yorksj.ac.uk\u0015, \u0013 HYPERLINK \"mailto:m.odea@yorksj.ac.uk\" \u0014m.odea@yorksj.ac.uk\u0015\nAbstract\nAn earthquake is one of the deadliest natural disasters. Forecasting an earthquake is a challenging task since natural causes such as movement of tectonic plates, volcanic eruptions, rainfall, and tidal stress all play an important part in earthquakes. Earthquakes can also be caused by human beings, such as mining, dams, nuclear bomb testing, etc. Solar activity has also been suggested as a possible cause of earthquakes. Solar activity and earthquakes occur in different parts of the solar system, on the Sun’s surface and the Earth’s surface, separated by a huge distance. However, scientists have been trying to figure out if there are any links between these two seemingly unrelated occurrences since the 19th century. In this chapter, the authors explored the methods of how machine learning algorithms including k-nearest neighbour, support vector regression, random forest regression, and Long Short-Term Memory network can be applied to predict earthquake and to understand if there is a relationship between solar activity and earthquakes. The authors employed three types of solar activity: sunspots number, solar wind, and solar flares, as well as worldwide earthquake frequencies that ranged in magnitude and depth.\nKeywords: Supervised Learning, K-Nearest Neighbour, Support Vector regression, random Forest Regression, Long Short-Term Memory Network, Characteristics of Earthquakes, Sunspot Number, Solar Wind, Solar Flares\nBackground\nSince ancient times cataclysmic disasters such as droughts, floods, earthquakes, volcanic eruptions, storms, and many other types of natural catastrophes, have had a profound impact on humans, at the cost of countless lives. These disasters are classified as natural disasters (Wirasinghe et al., 2013). The most severe natural disaster in recent history was the flood of the Yangtze–Huai River in China, in summer 1931. Up to 25 million people were affected by the effects of this flood (National Flood Relief Commission, 1933), hence it is considered the deadliest natural disasters since 1900 excluding epidemics and famines.\nThe number of deaths from natural disasters may change depending on the type of disaster and the affected area. But, from the average point of view, around 40,000 people per year are killed by natural disasters. For example, \u0013 REF _Ref75535711 \\h  \\* MERGEFORMAT \u0014Figure 1\u0015 shows the yearly average of global annual deaths from natural disasters between 1900 and 2010s. The graph was created based on data from (OFDA/CRED International Disaster Data, 2021).\nFigure \u0013 SEQ Figure \\* ARABIC \u00141\u0015 Yearly average global of annual deaths from natural disasters, by decade.\nAs seen in \u0013 REF _Ref75535711 \\h  \\* MERGEFORMAT \u0014Figure 1\u0015 the three deadliest natural disasters are droughts, floods, and earthquakes. However, in the last decades, the most dangerous natural disasters for people are considered to be earthquakes, extreme temperature, and floods. Even though the average global death toll from natural disasters in the 21st century is less than in the previous century, the average death rate is still high.\nMost of the Earth's meteorological processes are localised, and they make good weather forecasts only in a limited area. Space weather is always global on the planetary scale (Koskinen et al., 2001).\nFurther, the assumption that solar activities could have an influence on Earth’s natural disasters is not new. Back in 1853 the astronomer Wolf (1853) suggested sunspots might influence earthquake events. Since then, several studies, using statistical methods, have showed the correlati","cbCaiiaBwTIfU1Ax","https://ap.wps.com/l/cbCaiiaBwTIfU1Ax","docx",1358731,1,21,"English","en",105,"# Abstract\n# Background\n## Natural disasters and earthquake impact\n## Trends in global disaster deaths\n## Solar activity and proposed links to earthquakes\n## Growth of data and need for machine learning","[{\"question\":\"Why is earthquake forecasting considered challenging?\",\"answer\":\"Forecasting is difficult because multiple natural and human-related factors can influence earthquakes, including tectonic plate movement, volcanic eruptions, rainfall, tidal stress, and mining or dam-related activity.\"},{\"question\":\"Which machine learning methods are used to predict earthquakes in the chapter?\",\"answer\":\"The chapter applies k-nearest neighbour, support vector regression, random forest regression, and a Long Short-Term Memory (LSTM) network.\"},{\"question\":\"What solar activity and earthquake data are used for analysis?\",\"answer\":\"Solar activity is represented by sunspots number, solar wind, and solar flares, while earthquakes are represented by worldwide frequencies across different magnitudes and depths.\"}]","The Application of Machine Learning for Predicting Global Seismicity - 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