[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118138-en":3,"doc-seo-118138-105":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},118138,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Prediction Of Water Quality Using Effective Machine Learning Techniques","Water quality is essential for human health and for sustaining life, yet it has deteriorated over recent decades due to pollution and multiple environmental pressures. This study develops machine learning models to predict water quality and to perform water quality classification (WQC). Six algorithms—Naive Bayes, Random Forest, Gradient Boosting, k-nearest neighbor, Logistic Regression, and Decision Tree—are trained using 16 input parameters. Results show the Random Forest model achieves the best performance among the compared approaches.","Prediction Of Water Quality Using Effective Machine Learning Techniques  \nUzma Aman1, Dr. Fariha Ashfaq1  \n1 Department of Computer Science, Islamia University Bahawalpur, Pakistan  \n\n| ARTICLE INFO |  | ABSTRACT |\n| --- | --- | --- |\n| Article History:\u003Cbr>Received: April\u003Cbr>Revised: April\u003Cbr>Accepted: May\u003Cbr>Available Online: May | 24, 2024 30, 2024 03, 2024\u003Cbr>05, 2024 | One of the most vital natural resources for all earth's living things is water. Life's fundamental need is access to clean water. Water quality has substantially declined over the previous few decades as a result of pollution and numerous other problems. In this study, machine learning (ML) algorithms are developed to predict water quality and water quality classification (WQC) . For the prediction of water quality classification, six machine learning algorithms Naïve Bayes, Random Forest (RF), Gradient Boosting (GBoost), K-nearest neighbor (KNN),Logistic Regression (LogR), and Decision Tree (DT), have been used. The models were evaluated based on 16 parameters. The machine learning model’s result demonstrates the Random Forest model out performed than the other models. |\n| Keywords:\u003Cbr>Machine learning\u003Cbr>Water quality\u003Cbr>Environmental sciences\u003Cbr>Prediction\u003Cbr>Comparative Analysis Classification Codes:\u003Cbr>Funding:\u003Cbr>This research received no specific grant from any funding agency in the public or not-forprofit sector. |  |  |\n|  |  | © 2023 The authors published by JCIS. This is an Open Access Article under the Creative Common Attribution Non-Commercial 4.0 |\n| Corresponding Author’s Email: [fariha.ashfaq@iub.edu.pk](fariha.ashfaq@iub.edu.pk)\u003Cbr>Citation: |  |  |\n\n1. Introduction  \nThe most prevalent chemical that is continually recycled inside the human body is water [1] . Water is the most crucial resource because all forms of life must exist, but it is also constantly in danger of being contaminated by those same lives[2] [3] .The standard of people's drinking water has a significant impact on their health [4] . The availability of water for drinking both for home and industrial use determines a country's level of development [5] . Pollution from both domestic and industrial sources had a greater effect on water quality[6] . Policymakers and public health authorities who conduct activities for the prevention of water pollution and the preservation of public health may find this study interesting [7] . Many people in developing nations are now more likely to suffer from water-related illnesses [8] . Contaminated drinking water may cause very serious consequences that harm people's health, the environment, and infrastructure. A United Nations (UN) estimate [9] states that illnesses brought on by tainted water kill up to 1.5 million people each year. Eighty percent of health problems in developing countries are reportedly caused by contaminated water. Five million deaths and 2.5 billion illnesses are reported annually. Thus, it is critical to propose novel methods for  \nassessing and, if feasible, forecasting the water quality (WQ)[9] . The assessment and improvement of water sources are of particular importance because of serious health issues connected to the quality of drinking water [10] . Furthermore, early control of intelligent aquaculture requires the ability to forecast changes in water quality [11].Having water of good quality means reducing costs associated with using it for drinking, industrial purposes, and enhancing agricultural output [12] . The wider understanding of this problem is still inadequate, especially in nations where water supply outages are relatively modest[13] . Numerous water management groups that control water have installed monitoring stations to track changes in the water quality status up until the present [14] . It might be feasible to accurately determine the degree of contamination in a water resource using real-time observation of the ensuing variation in water quality [15]. An efficient treatment method should meet sever","cbCaiawxp8KDVP8Z","https://ap.wps.com/l/cbCaiawxp8KDVP8Z","pdf",450834,1,18,"English","en",105,"# Introduction\n## Problem background and health impact\n## Need for assessment and forecasting\n## Role of machine learning in water quality modeling\n# Methods and models overview\n## Algorithms used for WQC and prediction\n## Evaluation based on multiple parameters","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To develop machine learning models that predict water quality and classify water quality (WQC) using multiple parameters.\"},{\"question\":\"Which machine learning algorithms are used?\",\"answer\":\"Naive Bayes, Random Forest, Gradient Boosting, K-nearest neighbor, Logistic Regression, and Decision Tree.\"},{\"question\":\"How were the models evaluated and what was the best result?\",\"answer\":\"Models were evaluated using 16 parameters, and the Random Forest model outperformed the other models.\"}]","Prediction Of Water Quality Using Effective Machine Learning Techniques | PDF",1785681831,45,{"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},"prediction-of-water-quality-using-effective-machine-learning-techniques","",{"@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/prediction-of-water-quality-using-effective-machine-learning-techniques/118138/",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","2026-08-02",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 the study?","Question",{"text":75,"@type":76},"To develop machine learning models that predict water quality and classify water quality (WQC) using multiple parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used?",{"text":80,"@type":76},"Naive Bayes, Random Forest, Gradient Boosting, K-nearest neighbor, Logistic Regression, and Decision Tree.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the models evaluated and what was the best result?",{"text":84,"@type":76},"Models were evaluated using 16 parameters, and the Random Forest model outperformed the other models.","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"]