[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119816-en":3,"doc-seo-119816-105":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},119816,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Predicting Kereh River's Water Quality - A comparative study of machine learning models","This study presents a machine learning framework to predict the water quality of the Kereh River and classify outcomes as “polluted” or “slightly polluted.” Using datasets collected from 2010 to 2019, three algorithms—decision tree, random forests, and boosted regression tree—are trained and compared on multiple performance criteria. The random forest model achieves the best overall results with 97.30% accuracy, 100.00% sensitivity, 94.74% specificity, and 95.00% precision. Dissolved oxygen (DO) is identified as the dominant variable driving predictions.","[Available Online at www.e-iph.co.uk](Available Online at www.e-iph.co.uk)[ ](Available Online at www.e-iph.co.uk)Indexed in Clarivate Analytics WoS, and ScienceOPEN  \nKICSS2023  \nKedah International Conference on Social Science and Humanities UiTM Kedah (Online), Malaysia, 21-22 June 2023:  \n2nd International Conference on Business, Finance, Management and Economics  \n(BIZFAME)  \nPredicting Kereh River's Water Quality: A comparative study of machine learning models  \nNorashikin Nasaruddin1, Afida Ahmad1*, Shahida Farhan Zakaria1, Ahmad Zia Ul-Saufie2, Mohamed Syazwan Osman3  \n*Corresponding Author  \n1* College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Kedah Branch, 08400 Merbok, Kedah, Malaysia  \n2 School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia  \n3 EMZI-UiTM Nanoparticles Colloids & Interface Industrial Research Laboratory (NANO-CORE), Chemical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Pulau Pinang,  \nMalaysia.  \n[norashikin116@uitm.edu.my](norashikin116@uitm.edu.my), *[afidaahmad@uitm.edu.my](afidaahmad@uitm.edu.my), [shahidafarhan@uitm.edu.my](shahidafarhan@uitm.edu.my), [ahmadzia101@uitm.edu.my](ahmadzia101@uitm.edu.my), [syazwan.osman@uitm.edu.my](syazwan.osman@uitm.edu.my)  \nTel: +60175175881  \nAbstract  \nThis study introduces a machine learning-based approach to forecast the water quality of the Kereh River and categorize it into 'polluted' or 'slightly polluted' classifications. This work employed three machine learning algorithms: decision tree, random forests (RF), and boosted regression tree, leveraging data spanning from 2010 to 2019. Through comparative analysis, the RF model emerged as the most efficient, boasting an accuracy of 97.30%, sensitivity of 100.00%, specificity of 94.74%, and precision of 95.00% . Notably, the RF model identified dissolved oxygen (DO) as the paramount variable influencing water quality predictions.  \nKeywords: Water quality; machine learning; decision tree; random forest  \neISSN: 2398-4287 © 2023. The Authors. Published for AMER and cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BYNC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), and cE-Bs (Centre for Environment-Behaviour Studies), College of Built Environment, Universiti Teknologi MARA, Malaysia  \nDOI: [https://doi.org/10.21834/e-bpj.v8iSI15.5097](https://doi.org/10.21834/e-bpj.v8iSI15.5097)  \n1.0 Introduction  \nWater is fundamental to the sustenance of all life forms. Despite Malaysia's rich water resources, the issue of water pollution poses a severe threat, potentially leading to water shortages (Gasim et al. , 2013) . Rapid economic expansion and urban development, particularly in areas like the Klang Valley and Langat Valley, are significant contributors to this deterioration in water quality (Rahman, 2021) . Such activities are not only detrimental to ecological health but can also have far-reaching impacts on ecosystems and human well-being. The Malaysian government employs the water quality index to gauge pollution levels, using the national water quality standard (NWQS) to determine the suitability for water use. This index encompasses six parameters: dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-NL), suspended solid (SS), and pH. In a 2020 survey of 672 Malaysian rivers, 66% exhibited clean water quality, with 29% slightly polluted and 5% showing signs of pollution (Department of Environment Malaysia, 2020) . Given the intricate nature of water quality index calculations, they are time-intensive and complex (Bui et al. , 2020) . Ho","cbCaiiPdEEoG45iB","https://ap.wps.com/l/cbCaiiPdEEoG45iB","pdf",454897,1,7,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n# Methodology\n## Data collection\n## Machine learning models\n# Results and Discussion\n# Conclusion","[{\"question\":\"What is the main goal of predicting Kereh River's water quality in this study?\",\"answer\":\"The study aims to forecast the river’s water quality and categorize it into “polluted” or “slightly polluted” classes using machine learning models.\"},{\"question\":\"Which machine learning algorithms are compared, and what data period is used?\",\"answer\":\"The study compares decision tree, random forests, and boosted regression tree using data spanning from 2010 to 2019.\"},{\"question\":\"Which model performs best, and which variable most influences the predictions?\",\"answer\":\"Random forests performs best overall, and dissolved oxygen (DO) is identified as the most important variable affecting water quality predictions.\"}]","Predicting Kereh River's Water Quality - A comparative study of machine learning models | PDF",1785726453,18,{"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},"predicting-kereh-rivers-water-quality-a-comparative-study-of-machine-learning-models","",{"@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/predicting-kereh-rivers-water-quality-a-comparative-study-of-machine-learning-models/119816/",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},"What is the main goal of predicting Kereh River's water quality in this study?","Question",{"text":75,"@type":76},"The study aims to forecast the river’s water quality and categorize it into “polluted” or “slightly polluted” classes using machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared, and what data period is used?",{"text":80,"@type":76},"The study compares decision tree, random forests, and boosted regression tree using data spanning from 2010 to 2019.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best, and which variable most influences the predictions?",{"text":84,"@type":76},"Random forests performs best overall, and dissolved oxygen (DO) is identified as the most important variable affecting water quality predictions.","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"]