[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118217-en":3,"doc-seo-118217-105":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},118217,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning for Water Quality Index Forecasting - Research Report","Study forecasts Water Quality Index (WQI) for the Tumkur district in Karnataka, India to support pollution reduction efforts. It applies machine learning models—including support vector machines, regression trees, linear regression, and neural networks—to WQI computation. WQI is derived from parameters such as total hardness, pH, alkalinity, turbidity, chloride, dissolved solids, and conductivity. Data are split 80:20 for training and testing, and model quality is evaluated using R². Results show support vector machines and linear regression achieve the strongest performance, with high R² values for both training and testing.","Machine Learning for Water Quality Index Forecasting  \nArun Kumar Thimalapur Doddabasappaar 1, Bilegowdanamane Earappa Yogendra 1, Prashanth  \nJanardhan2, Prema Nisana Siddegowda3,*  \n1 Department of Civil Engineering, Kalpataru Institute of Technology, Tiptur, India  \n2 Department of Civil Engineering, National Institute of Technology of Silchar, Assam, India  \n3Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India  \nReceived 19 September 2023; received in revised form 11 January 2024; accepted 12 January 2024  \nDOI: [https://doi.org/10.46604/emsi.2024.12870](https://doi.org/10.46604/emsi.2024.12870)  \nAbstract  \nThis study aims to forecast water quality in the Tumkur district, Karnataka state, India, to increase pollution levels. Various machine learning techniques, including support vector machines, regression trees, linear regression, and neural networks, are employed. The Water Quality Index (WQI) is determined using parameters such as total hardness, pH, alkalinity, turbidity, chloride, dissolved solids, and conductivity. The dataset is split into training and testing sets (80:20) to assess model performance. Support Vector Machines and Linear Regression outperform other models, achieving R2 values of 0.96 and 0.99 for training and testing, respectively. This research underscores the importance of advanced machine learning techniques for accurate water quality prediction, crucial for effective pollution reduction strategies in the region.  \nKeywords: water quality index, machine learning, random forest, support vector machine  \n1. Introduction  \nThe availability of safe fresh water for agricultural, human use, and aquatic ecosystems, is all significantly impacted by the decline of water quality. Developing nations regularly undergo times of fast economic expansion, and every development project has the potential to have negative environmental repercussions. The pressure on the natural fertility of soils rises as a rapidly rising population and wealth, frequently leading to over-extraction of nutrients and requiring the use of artificial fertilizers. Extra fertilizer frequently finds its way into groundwater and waterways. Rivers constantly carry contaminants to lakes and oceans, harming ecosystems and endangering human health. Therefore, monitoring and evaluating water quality are essential for efficient, sustainable water management as well as for maintaining both human and environmental health.  \nThe unitless index WQI is derived by selecting specific water quality parameters. These measures offer a categorical assessment of the historical and current water quality of bodies of water. Examples of common variables include Ca2+, Mg2+, NO-3, and other elements frequently utilized to forecast the WQIs, such as dissolved oxygen, pH, temperature, and total suspended solids [1] The WQI is highly beneficial in guiding the decisions and actions of decision-makers. However, the calculation ofWQI is not straightforward because sub-indices are computed within WQI equations.  \nBecause WQIs typically include distinct equations, computing WQI has the drawbacks of being time-consuming, tedious, challenging, and inconsistent. There is no single WQI methodology, as this discussion may have made clear. The current study endeavors to apply soft computing approaches to predict the water quality of a supply system in this context. The goal of the work is to propose a reliable method for accurately predicting water quality using machine learning technology. Fig. 1 illustrates a diagrammatic representation of the current investigation.  \n* [Corresponding author. E-mail address: prema.gowda@gmail.com](Corresponding author. E-mail address: prema.gowda@gmail.com)  \nThe goal of the study is to examine water quality data to gain a deeper understanding of a specific body of water's condition and characteristics. The biological, chemical, and physical properties of water constitute its quality, determ","cbCaij7q9hWpvEEz","https://ap.wps.com/l/cbCaij7q9hWpvEEz","pdf",669163,1,11,"English","en",105,"# Introduction\n## Water quality monitoring and need for WQI\n## Challenges in WQI computation\n## Objective and proposed machine learning approach","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To forecast water quality in the Tumkur district by predicting the Water Quality Index using machine learning techniques.\"},{\"question\":\"Which water quality parameters are used to determine WQI?\",\"answer\":\"WQI is computed using parameters such as total hardness, pH, alkalinity, turbidity, chloride, dissolved solids, and conductivity.\"},{\"question\":\"How is model performance evaluated in the study?\",\"answer\":\"The dataset is split into training and testing sets in an 80:20 ratio, and performance is assessed using R² values.\"}]","Machine Learning for Water Quality Index Forecasting - Research Report | PDF",1785682315,28,{"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},"machine-learning-for-water-quality-index-forecasting-research-report","",{"@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/machine-learning-for-water-quality-index-forecasting-research-report/118217/",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 this study?","Question",{"text":75,"@type":76},"To forecast water quality in the Tumkur district by predicting the Water Quality Index using machine learning techniques.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which water quality parameters are used to determine WQI?",{"text":80,"@type":76},"WQI is computed using parameters such as total hardness, pH, alkalinity, turbidity, chloride, dissolved solids, and conductivity.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated in the study?",{"text":84,"@type":76},"The dataset is split into training and testing sets in an 80:20 ratio, and performance is assessed using R² values.","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"]