[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121288-en":3,"doc-seo-121288-105":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},121288,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Evaluation of Machine Learning in Predicting Air Quality Index - Article","Environmental pollution creates serious health risks, and Malaysia faces a growing air quality challenge driven by rapid urbanization and industrialization. The Air Quality Index (AQI) is a standard metric for air pollution, and machine learning can support accurate AQI prediction. This study evaluates how key AQI components—PM2.5, NO2, CO, and O3—affect AQI using 125 random locations across Malaysia. Generalized Linear Model, Decision Tree, and Support Vector Machine are assessed, with PM2.5 showing the strongest influence. All models achieve high accuracy with R² above 90% and low errors (MAE and RMSE under 2).","Mathematical Sciences and Informatics Journal  \nVol. 4, No. 1, May 2023, pp. 1-10  \n[http://www.mijuitmjournal.com](http://www.mijuitmjournal.com) DOI : 10 .24191/mij.v4i1 .21889  \nEvaluation of Machine Learning in Predicting Air Quality Index  \nAbdullah Sani Abdul Rahman  \nFaculty of Sciences and Information Technology, Univesiti Teknologi Petronas, Perak, Malaysia  \n[sani.arahman@utp.edu.my](sani.arahman@utp.edu.my)  \nAizal Yusrina Idris  \nYanbu Industrial College, Kingdom of Saudi Arabia  \n[idrisa@rcyci.edu.sa](idrisa@rcyci.edu.sa), [aizalyusrina@gmail.com](aizalyusrina@gmail.com)  \nSuhaimi Abdul Rahman  \nYanbu Technical Institute, Kingdom of Saudi Arabia  \n[abulrahmans@rcyci.edu.sa](abulrahmans@rcyci.edu.sa), [suhaimi.ar@gmail.com](suhaimi.ar@gmail.com)  \nArticle Info ABSTRACT  \n\n| Article history:\u003Cbr>Received Feb 15, 2023 Revised Mac 25, 2023 Accepted Apr 12, 2023 | Environmental pollution poses significant health risks, and Malaysia is facing a critical air pollution issue due to the rapid growth of urbanization and industrialization. The Air Quality Index (AQI) is a standard measure of air pollution, and machine learning methods have shown promise in accurately predicting AQI levels. However, there is limited research on the application of intelligent approaches to predict AQI in Malaysia. This research investigates the impact of various AQI components, including Particulate Matter 2.5 (PM2.5), Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Ozone (O3), using 125 random locations across Malaysia, ranging from the north to the southern regions. Three machine learning algorithms, namely Generalized Linear Model, Decision Tree and Support Vector Machine are used in this research. The results show that PM2 .5 has the most significant impact on AQI levels among all components analyzed, and all selected machine learning algorithms exhibit high prediction accuracy, with R^ above 90% and low prediction errors (less than 2 MAE and RMSE) . This research provides essential insights into predicting AQI levels using machine learning approaches and highlights the critical role of PM2.5 in determining AQI levels in Malaysia. The findings can aid authorities in obtaining rapid and accurate information to effectively manage air pollution in the country. |\n| --- | --- |\n| Keywords (minimum 5):\u003Cbr>Air Quality Index (AQI) Generalized Linear Model Decision Tree\u003Cbr>Support Vector Machine Prediction |  |\n\nCorresponding Author:  \nAizal Yusrina Idris  \nYanbu Industrial College, Kingdom of Saudi Arabia  \nemail: [idrisa@rcyci.edu.sa](idrisa@rcyci.edu.sa) , [aizalyusrina@gmail.com](aizalyusrina@gmail.com)  \n1. Introduction  \nEnvironmental pollution is a critical global issue that has adverse impacts on human health, the natural environment, and the economy[1],[2] . The World Health Organization (WHO) estimates that air pollution causes seven million premature deaths each year worldwide[3] . Malaysia is not exempt from this issue and faces severe air pollution problems due to rapid urbanization and industrialization. According to the IQAir AirVisual 2019 World Air Quality Report[4], several Malaysian cities ranked among the world's most polluted cities in terms of air quality. This highlights the urgent need to develop effective measures to manage and control air pollution in Malaysia.  \nAir Quality Index (AQI) is a standard measure of air quality that provides information about the air quality in a particular region. The AQI is calculated based on the concentrations of several air pollutants, including Particulate Matter 2.5 (PM2.5), Nitrogen Dioxide (NO2), Carbon monoxide (CO), and Ozone (O3)[5],[6] . PM2 .5 is a type of air pollutant with a diameter of 2 .5 micrometers or  \nThis is an open access article under a Creative CommonsAttributionShareAlike4 .0 International License (CC BY-SA 4 . 0) .  \nless. PM2 .5 can penetrate deeply into the respiratory system, causing adverse health effects, such as heart disease, stroke, lung cancer, and asthma.","cbCailxF1Hi9BXCD","https://ap.wps.com/l/cbCailxF1Hi9BXCD","pdf",1564512,1,11,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"Which AQI components are analyzed in this research?\",\"answer\":\"The study evaluates PM2.5, NO2, CO, and O3 as influencing components for AQI prediction.\"},{\"question\":\"What machine learning algorithms are used to predict AQI?\",\"answer\":\"The research applies Generalized Linear Model, Decision Tree, and Support Vector Machine.\"},{\"question\":\"What is the most significant component affecting AQI in Malaysia?\",\"answer\":\"PM2.5 has the most significant impact on AQI levels among the analyzed components.\"}]","Evaluation of Machine Learning in Predicting Air Quality Index - Article | PDF",1785734920,28,{"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},"evaluation-of-machine-learning-in-predicting-air-quality-index-article","",{"@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/evaluation-of-machine-learning-in-predicting-air-quality-index-article/121288/",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 AQI components are analyzed in this research?","Question",{"text":75,"@type":76},"The study evaluates PM2.5, NO2, CO, and O3 as influencing components for AQI prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning algorithms are used to predict AQI?",{"text":80,"@type":76},"The research applies Generalized Linear Model, Decision Tree, and Support Vector Machine.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the most significant component affecting AQI in Malaysia?",{"text":84,"@type":76},"PM2.5 has the most significant impact on AQI levels among the analyzed components.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]