[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124272-en":3,"doc-seo-124272-105":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},124272,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Earthquake Epicenter Prediction from the Java-Bali Radon Gas Telemonitoring Station Using Machine Learning - Early Warning System Algorithm","Predicting earthquake epicenter locations is essential for forecasting seismic events and improving mitigation planning, yet uncertainty in epicenter determination limits effective disaster risk reduction, especially along major plate fault systems. This study develops a supervised machine learning algorithm that uses variations in radon gas concentrations from six Java-Bali telemonitoring stations to estimate the distance to the impending epicenter. Among evaluated ensemble models, random forest achieves the lowest average root mean square error of 453.10 km, supporting more actionable early warning and preparedness strategies.","Earthquake epicenter prediction from the Java-Bali radon gastelemonitoring station using machine learning  \nChristophorus Arga Putranto, Sunarno, Faridah, Thomas Oka Pratama  \nSensor and Tele-control Laboratory, Department of Nuclear Engineering and Engineering Physics, Faculty of Engineering ,  \nUniversitas Gadjah Mada, Sleman, Indonesia  \nArticle history:  \nReceived Mar 21, 2024 Revised Sep 29, 2024 Accepted Oct 8, 2024  \nKeywords:  \nEarly warning system Earthquake prediction Location  \nMachine learning Radon  \nCorresponding Author:  \nPredicting the location of earthquake epicenters is a critical aspect of earthquake forecasting, as it complements efforts to determine the time and magnitude of seismic events. This research addresses the challenge posed by the uncertainty in epicenter locations, particularly along the extensive plate faults of Indo-Australia and Eurasia. In these regions, effective earthquake prediction is compromised without accurate epicenter information, impeding mitigation strategies and complicating disaster impact estimation. The primary objective of this study is to devise an algorithm for forecasting earthquake epicenter locations by harnessing variations in radon gas concentrations on southern Java Island, Indonesia, as a predictive precursor. Using a supervised machine learning approach, this study integrates radon gas concentration data to predict the distance between a radon gastelemonitoring station and the impending earthquake epicenter. Three distinct machine learning algorithms were evaluated using data from six Java-Bali radon gas telemonitoring stations within an early warning system. The random forest algorithm emerged as the most effective, yielding an average root mean square error of 453.10 kilometers. The findings of this research significantly contribute to earthquake risk mitigation efforts. This work enhances our capability to anticipate seismic events, and more effective disaster preparedness and response strategies in earthquake-prone regions.  \nThis is an open access article under the CC BY-SA license.  \nSunarno  \nSensor and Tele-control Laboratory, Department of Nuclear Engineering and Engineering Physics Faculty of Engineering, Universitas Gadjah Mada  \nBulaksumur, Depok, Sleman Regency, Special Region of Yogyakarta 55281 , Indonesia  \nEmail: [sunarno@ugm.ac.id](sunarno@ugm.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nEarthquakes are among the most deadly and dangerous natural disasters, mainly caused by tectonic activity between the earth's plates. Despite many efforts to understand and predict earthquakes, accurate prediction remains a significant challenge in geophysical science. The lack of references, methods, models, calculations, indicators, and information needed for earthquake prediction is a significant obstacle to overcoming this complex phenomenon [1], [2] . One approach in efforts to predict earthquakes is to look for indicators or precursors that can provide initial clues that an earthquake will occur. Some known indicators include natural events, atmospheric conditions, groundwater fluctuations, gas emissions in the soil, and animal responses. Among these precursors, fluctuations in radon gas emissions in soil have attracted attention, and several studies have linked them to potential as an earthquake indicator. For example, radon  \ngas precursors have been observed as earthquake precursors by Urumu Tsunogai and colleagues in the Kobe, Japan, earthquake in 1995 [3]–[10] .  \nThe radon gas monitoring can potentially observe the environment as a precursor to earthquakes. This method can be simulated in the laboratory or carried out long-term with direct observation through various devices and sensors [6]–[8], [11]–[16] . The early warning system engineering physics research team at Universitas Gadjah Mada has researched using multi-device observation stations spread around Yogyakarta, Indonesia. The radon gas data collected from these stations is vital for deve","cbCaiu9WjB8qlHRm","https://ap.wps.com/l/cbCaiu9WjB8qlHRm","pdf",398509,1,7,"English","en",105,"# Introduction\n# Research Method","[{\"question\":\"What problem does the study address in earthquake forecasting?\",\"answer\":\"The study targets uncertainty in earthquake epicenter locations, which restricts effective prediction and weakens mitigation and impact assessment efforts.\"},{\"question\":\"How does the proposed method use radon gas data?\",\"answer\":\"It leverages variations in radon gas concentrations measured by six Java-Bali telemonitoring stations to predict the distance between the station and the impending epicenter.\"},{\"question\":\"Which machine learning model performs best, and what is its error?\",\"answer\":\"The random forest algorithm performs best, producing an average root mean square error of 453.10 kilometers.\"}]","Earthquake Epicenter Prediction from the Java-Bali Radon Gas Telemonitoring Station Using Machine Learning - Early Warning System Algorithm | PDF",1785821312,18,{"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},"earthquake-epicenter-prediction-from-the-java-bali-radon-gas-telemonitoring-station-using-machine-learning-early-warning-system-algorithm","",{"@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/earthquake-epicenter-prediction-from-the-java-bali-radon-gas-telemonitoring-station-using-machine-learning-early-warning-system-algorithm/124272/",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","2026-08-04",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},"What problem does the study address in earthquake forecasting?","Question",{"text":75,"@type":76},"The study targets uncertainty in earthquake epicenter locations, which restricts effective prediction and weakens mitigation and impact assessment efforts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method use radon gas data?",{"text":80,"@type":76},"It leverages variations in radon gas concentrations measured by six Java-Bali telemonitoring stations to predict the distance between the station and the impending epicenter.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best, and what is its error?",{"text":84,"@type":76},"The random forest algorithm performs best, producing an average root mean square error of 453.10 kilometers.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"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"]