[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123622-en":3,"doc-seo-123622-105":29,"detail-sidebar-cat-0-en-105":82},{"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},123622,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Potable Water Identification with Machine Learning - An Exploration of Water Quality Parameters","This research develops machine learning methods to determine water potability from water quality measurements. Three classification algorithms—decision tree, gradient boosting, and bagging classifier—are trained and evaluated on the same dataset. The results show gradient boosting achieves the best F1-score of 0.78, indicating stronger ability to correctly classify both safe and contaminated samples. The study concludes gradient boosting is a promising, reliable approach for water quality assessment.","Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters  \n1B R Mohan, 2Dr. Dileep M, 3Dr Vijay Bhuria, 4Sai Sudha Gadde, 5Dr. Kumarasamy M, 6Achyutha Prasad N  \n1Computer Science and Engineering, East West Institute of Technology, Bangalore  \n[brmohan398@gmail.com](brmohan398@gmail.com)  \n2Strategy, Faculty of Management Sciences, Nile University of Nigeria, Abuja.  \n[Email: Dileep.KM@nileuniversity.edu.ng](Email: Dileep.KM@nileuniversity.edu.ng)  \n3Electrical Department, Madhav Institute of Technology & Science, Gwalior, India.  \n[Email: vijay.bhuria@mitsgwalior.in](Email: vijay.bhuria@mitsgwalior.in)  \n4Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, A.P., India.  \nEmail: [Saisudhagadde@gmail.com](Saisudhagadde@gmail.com)  \n5Department of Computer Science, College of Engineering and Technology,  \nWollega University, Nekemte, Oromia Region, Ethiopia.  \nEmail: [drmksamy115@gmail.com](drmksamy115@gmail.com)  \n6Department of Computer Science and Engineering, East West Institute of Technology, Bangalore, India  \nEmail: [achyuth001@gmail.com](achyuth001@gmail.com)  \nAbstract-In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting is a promising approach for determining water potability and can be used as a reliable method for water quality assessment.  \nKeywords-Water Quality; Machine Learning; Water Potability; Classification  \nI. INTRODUCTION  \nWater potability refers to the quality of water that is safe for human consumption. It is a crucial aspect of public health and sanitation, such as having access to sanitary drinking water is essential for maintaining a healthy lifestyle. Water contamination can occur due to a variety of factors such as industrial and agricultural pollution, natural disasters, and improper sewage treatment. Contaminated water can cause a range of health issues, including diarrhea, cholera, and typhoid fever [1-4] . In severe cases, it can lead to death. To ensure water potability, water is tested for a variety of contaminants, including bacteria, viruses, and chemicals such as lead and arsenic. These tests are conducted by government agencies and independent organizations, and the results are made available to the public. However, even with regular testing, it can be difficult to detect all contaminants in water [5-8] . This is where machine learning comes in. Machine learning is able to recognise patterns and trends in water quality data that may not be immediately obvious to human researchers by applying sophisticated algorithms. In the end, enhanced water potability can result from the more precise and effective detection of water pollutants. In addition to the  \npublic health benefits, ensuring water potability also has economic benefits. Because it supports increased agricultural production, industrial development, and tourism, access to clean, safe drinking water is crucial for economic progress. Overall, water potability is a critical issue that affects the health and well-being of individuals and communities. The use of machine learning in potability detection can help improve the accuracy and efficiency of water testing, ensuring access to clean and safe drinking water for all. Machine learning is a rapidly growing field that is revolutionizing many industries, including manufacturing, healthcare, and transportation. In manufacturing, machine learning is us","cbCaicV2FwEEIM1j","https://ap.wps.com/l/cbCaicV2FwEEIM1j","pdf",1224509,1,"English","en",105,"# Introduction\n# Problem Statement","[{\"question\":\"Why is gradient boosting considered the most effective in the results?\",\"answer\":\"Gradient boosting achieves the highest F1-score (0.78), indicating the best performance in correctly identifying both safe and contaminated water samples.\"}]","Potable Water Identification with Machine Learning - An Exploration of Water Quality Parameters | PDF",1785817691,20,{"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":77,"head_meta":79,"extra_data":81,"updated_unix":27},"potable-water-identification-with-machine-learning-an-exploration-of-water-quality-parameters","",{"@graph":35,"@context":76},[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/potable-water-identification-with-machine-learning-an-exploration-of-water-quality-parameters/123622/",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","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"Why is gradient boosting considered the most effective in the results?","Question",{"text":74,"@type":75},"Gradient boosting achieves the highest F1-score (0.78), indicating the best performance in correctly identifying both safe and contaminated water samples.","Answer","https://schema.org",{"og:url":51,"og:type":78,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":80,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,111,114,118,121,125],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":45,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":45,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":28,"slug":117},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":28,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":97,"slug":128},19,"General","general"]