[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118402-en":3,"doc-seo-118402-105":29,"detail-sidebar-cat-0-en-105":90},{"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":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},118402,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Prediction of earthquake by machine learning models and neural network - Accuracy and alert using multiple models","Earthquake occurrence is explored from multiple perspectives because it strikes without warning and can cause severe destruction, casualties, and major economic losses. This work applies machine learning and a fully connected neural network to predict both earthquake magnitude and alert levels. An online dataset is collected and processed, missing values are removed, and class imbalance is addressed before training four classical classifiers and a neural network. Final predictive accuracy results are presented at the end of the study.","Prediction of earthquake by machine learning models and neural network  \nBohong Xu  \nSchool of Artificial Intelligence, South China Normal University, 510170 GuangZhou, China  \nAbstract. The occurrence of earthquakes has been examined from many aspects. Obviously, earthquakes occur without any warning and can damage entire cities in just a moment, causing enormous casualties and large amount of economic loss. In the last few years, people have given great effort to examine the earthquake from many aspects with various methods. The use of machine learning has solved seemingly intractable problems, inspiring the prospect of finding suitable solutions to dilemmas that are now considered unsolvable. Earthquake prediction, in this article, is executed with a neural network and machine learning models. After reading many articles, the author discovered that not many articles use a fully connected neural network to predict earthquake magnitude. In this article, the author first process the dataset sought from the internet, then predict the magnitude and the alert of earthquakes, with four machine learning models and a neural network. After that, the result of the accuracy are shown at the end of the article.  \n1 Introduction  \nEarthquake is a kind of natural phenomenon caused by the activities of continental crust. With serious destruction to the surface of the earth, many infrastructures are easy to be damaged. Some earthquakes that can release a lot of energy can even cause the explosion of volcanoes or tsunami. These natural disasters can cause a large quantity of loss of possessionsand threaten people’s lives. Lots of effort was spent on the Parkfield prediction experiment [1] . The results gained from researches made the scientific community wonder whether earthquakes could be predicted [2]. This question is still a in discussion among experts. Some scientists conclude that earthquake cannot be predicted [3] . Literature has a limited amount of studies that specifically compare the performance of various models [4] . But now, lots of studies are developed with tools of machine learning and deep learning. Deep learning is quickly becoming one of the most effective tools, not just in data science but also for solving complex and challenging problems in physics. This is backed up by the exceptional effectiveness of deep learning in uncovering complex patterns in numerous datasets. Prediction of earthquakes allows refugees to escape to safe areas in advance. With the help of warning, the rate of survival is likely to raise up. As it is widely used in the newspaper report, earthquake magnitude is a common measure to describe the level of the earthquake.  \nCorresponding author: [20222034042@m.scnu.edu.cn](20222034042@m.scnu.edu.cn)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nMagnitude-frequency relationships are frequently studied in seismology, both through observation and theory. Their application is widespread in estimating the maximum magnitudes of future earthquakes, which is a crucial factor in earthquake prediction [5] . Magnitude is employed to measure the strength of the earthquake source through logarithmically calculations. And the public can know the strengthen quickly through the magnitude. Apart from the magnitude, the alert which is described in different kinds of color is also a convenient way for the refugees to know the strength of the earthquake. In this research, there are two methods to predict the alert and magnitude separately. One is the method of machine learning with four models. The other is building a neural network to predict the magnitude. Machine learning models consist of decision tree classifier, K-nearestsclassifier, random forest classifier, and gradient boosting classifier. All the algorithms have ","cbCaianndGazkfbp","https://ap.wps.com/l/cbCaianndGazkfbp","pdf",418080,1,"English","en",105,"# Introduction\n# Methodology\n## Data description of the machine learning model\n## Data processing","[{\"question\":\"What is the main goal of this research on earthquake prediction?\",\"answer\":\"The research aims to predict earthquake magnitude and the alert level separately using machine learning models and a neural network, then report the resulting accuracy.\"},{\"question\":\"Where does the dataset used in the study come from?\",\"answer\":\"The dataset is extracted from Kaggle and accessed through the internet.\"},{\"question\":\"How does the study handle data quality and alert distribution before training?\",\"answer\":\"It removes null values (e.g., in the magnitude feature) to avoid prediction errors and visualizes alert categories; class imbalance is addressed by increasing the minority class to better match the majority class.\"}]","Prediction of earthquake by machine learning models and neural network - Accuracy and alert using multiple models | PDF",1785683450,20,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":27},"prediction-of-earthquake-by-machine-learning-models-and-neural-network-accuracy-and-alert-using-multiple-models","",{"@graph":35,"@context":84},[36,53,67],{"@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/prediction-of-earthquake-by-machine-learning-models-and-neural-network-accuracy-and-alert-using-multiple-models/118402/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of this research on earthquake prediction?","Question",{"text":74,"@type":75},"The research aims to predict earthquake magnitude and the alert level separately using machine learning models and a neural network, then report the resulting accuracy.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Where does the dataset used in the study come from?",{"text":79,"@type":75},"The dataset is extracted from Kaggle and accessed through the internet.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the study handle data quality and alert distribution before training?",{"text":83,"@type":75},"It removes null values (e.g., in the magnitude feature) to avoid prediction errors and visualizes alert categories; 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