[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119472-en":3,"doc-seo-119472-105":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},119472,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","EFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS - EXAMPLE OF KAZAKHSTAN","This study investigates the effectiveness of machine learning methods in identifying earthquake-prone areas in Kazakhstan and neighboring regions. Using a comprehensive dataset covering major earthquakes from 1900 to 2023, multiple algorithms were evaluated, including RandomForest, GradientBoosting, Logistic Regression, SVC, KNN, Decision Tree, XGBoost, LightGBM, AdaBoost, and MLPClassifier, with a focus on predicting earthquake magnitudes and frequencies. Results show that XGBoost and RandomForest achieve the highest predictive accuracy, supporting their use in seismic risk assessment. A geographic heatmap visualizes high-risk zones to support safety and emergency preparedness planning, while highlighting the need to continuously improve models with additional environmental and geological factors.","62  \nScientific Journal of Astana IT University ISSN (P): 2707-9031 ISSN (E): 2707-904X VOLUME 21, MARCH 2025  \nDOI: 10.37943/21KUXZ6354  \nGulnur Kazbekova  \nCandidate of Technical Sciences, Associate Professor  \nHead of the Department of Computer Engineering [gulnur.kazbekova@ayu.edu.kz](gulnur.kazbekova@ayu.edu.kz), [orcid.org/0000-0002-2756-7926](orcid.org/0000-0002-2756-7926)[ ](orcid.org/0000-0002-2756-7926)[Khoja Akhmet Yassawi International Kazakh-Turkish University](Khoja Akhmet Yassawi International Kazakh-Turkish University), Kazakhstan  \nArypzhanAben  \nMaster of Science, Department of Computer Engineering [arypzhan.aben@ayu.edu.kz](arypzhan.aben@ayu.edu.kz), [orcid.org/0000-0001-8534-3288](orcid.org/0000-0001-8534-3288)[ ](orcid.org/0000-0001-8534-3288)[Khoja Akhmet Yassawi International Kazakh-Turkish University](Khoja Akhmet Yassawi International Kazakh-Turkish University), Kazakhstan  \nAnuarbekAmanov  \nPhD, Senior Lecturer, Department of Computer Engineering [anuarbek.amanov@ayu.edu.kz](anuarbek.amanov@ayu.edu.kz), [orcid.org/0000-0003-0638-6859](orcid.org/0000-0003-0638-6859)[ ](orcid.org/0000-0003-0638-6859)[Khoja Akhmet Yassawi International Kazakh-Turkish University](Khoja Akhmet Yassawi International Kazakh-Turkish University), Kazakhstan  \nNurseit Zhunissov  \nPhD, Senior Lecturer, Department of Computer Engineering [nurseit.zhunissov@ayu.edu.kz](nurseit.zhunissov@ayu.edu.kz), [orcid.org/0000-0001-6531-9408](orcid.org/0000-0001-6531-9408)[ ](orcid.org/0000-0001-6531-9408)[Khoja Akhmet Yassawi International Kazakh-Turkish University](Khoja Akhmet Yassawi International Kazakh-Turkish University), Kazakhstan  \nAimanAbibullayeva  \nPhD, Senior Lecturer, Department of Computer Engineering [aiman.abibullayeva@ayu.edu.kz](aiman.abibullayeva@ayu.edu.kz), [orcid.org/0000-0003-2449-2540](orcid.org/0000-0003-2449-2540)[ ](orcid.org/0000-0003-2449-2540)[Khoja Akhmet Yassawi International Kazakh-Turkish University](Khoja Akhmet Yassawi International Kazakh-Turkish University), Kazakhstan  \nEFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS:  \nEXAMPLE OF KAZAKHSTAN  \nAbstract: This study investigates the effectiveness of machine learning methods in identifying earthquake-prone areas in Kazakhstan and its neighboring regions. By leveraging a comprehensive dataset encompassing significant earthquake data from 1900 to 2023, various machine learning algorithms were employed, including RandomForest, GradientBoosting, Logistic Regression, Support Vector Classification (SVC), K-Nearest Neighbors (KNeighbors), Decision Tree, XGBoost, LightGBM, AdaBoost, and MLPClassifier. The primary objective was to analyze and compare the performance of these models in predicting earthquake magnitudesand frequencies. The results reveal that certain algorithms significantly outperformed others in terms of accuracy, underscoring the potential of machine learning techniques to enhance earthquake prediction capabilities. Notably, XGBoost and RandomForest demonstrated the highest predictive accuracy, suggesting their suitability for application in seismic risk assessment. These findings offer valuable insights for governmental agencies engaged in disaster management and prevention planning, highlighting the practical implications of integrating advanced analytical techniques in their strategies. In addition to model performance analysis, a visual heatmap was generated to illustrate the geographical distribution of earthquake occurrences across the studied regions. This visual representation effectively identifies highrisk areas, serving as a crucial tool for local authorities and researchers in making informed decisions regarding safety measures and emergency preparedness. This research contributes  \nCopyright © 2025, Authors. This is an open access article under the Creative Commons CC BY-NC-ND license Received: 17.10.2024 Accepted: 24.03.2025 Published: 30.03.2025  \nDOI: 10. 37943/21KUXZ6354  \n© Gulnur Kazbekova, Ary","cbCaif7GsHU2A66j","https://ap.wps.com/l/cbCaif7GsHU2A66j","pdf",1662675,1,16,"English","en",105,"# Introduction\n## Data and Methods\n## Model Performance and Comparison\n## Geographical Visualization (Heatmap)\n## Implications and Future Directions","[{\"question\":\"Which machine learning algorithms were evaluated for earthquake-prone area detection?\",\"answer\":\"The study evaluated RandomForest, GradientBoosting, Logistic Regression, SVC, K-Nearest Neighbors, Decision Tree, XGBoost, LightGBM, AdaBoost, and MLPClassifier.\"},{\"question\":\"What were the main evaluation objectives of the models?\",\"answer\":\"The models were analyzed and compared based on their ability to predict earthquake magnitudes and frequencies.\"},{\"question\":\"Which algorithms produced the highest predictive accuracy and why does it matter?\",\"answer\":\"XGBoost and RandomForest delivered the highest predictive accuracy, indicating strong suitability for seismic risk assessment and practical disaster management planning.\"}]","EFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS - EXAMPLE OF KAZAKHSTAN | PDF",1785724490,40,{"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},"effectiveness-of-machine-learning-methods-in-determining-earthquake-probable-areas-example-of-kazakhstan","",{"@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/effectiveness-of-machine-learning-methods-in-determining-earthquake-probable-areas-example-of-kazakhstan/119472/",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","2026-08-03",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 algorithms were evaluated for earthquake-prone area detection?","Question",{"text":75,"@type":76},"The study evaluated RandomForest, GradientBoosting, Logistic Regression, SVC, K-Nearest Neighbors, Decision Tree, XGBoost, LightGBM, AdaBoost, and MLPClassifier.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What were the main evaluation objectives of the models?",{"text":80,"@type":76},"The models were analyzed and compared based on their ability to predict earthquake magnitudes and frequencies.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithms produced the highest predictive accuracy and why does it matter?",{"text":84,"@type":76},"XGBoost and RandomForest delivered the highest predictive accuracy, indicating strong suitability for seismic risk assessment and practical disaster management planning.","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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","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"]