[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117917-en":3,"doc-seo-117917-105":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},117917,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Assessment of Water Quality using Machine Learning and Fuzzy Techniques","River water quality in the Ganga is critical for drinking, domestic use, irrigation, and aquatic ecosystems, yet rising pollution has degraded it through complex, uncertain decision factors and inherent subjectivity. This study applies machine learning and fuzzy techniques to build river water quality assessment models. Water quality is categorized into three classes, and seven models are evaluated using accuracy, precision, recall, and F1-score.","Assessment of Water Quality using Machine Learning and Fuzzy Techniques  \nShashi Kant 1, Devendra Agarwal2, Praveen Kumar Shukla3  \n1,2,3Artificial Intelligence Research Center, Department of CSE, School of Engineering, Babu Banarasi Das University, Lucknow, India  \n[1](1shashikant3245@gmail.com)[shashikant3245@gmail.com](1shashikant3245@gmail.com), [2](2devendragarwal@gmail.com)[devendragarwal@gmail.com](2devendragarwal@gmail.com), [3](3drpraveenkumarshukla@gmail.com)[drpraveenkumarshukla@gmail.com](3drpraveenkumarshukla@gmail.com)  \n\n| How to cite this paper: S. Kant, D. Agarwal and P. K. Shukla, “Assessment of Water Quality using Machine Learning and Fuzzy Techniques,” Journal of Informatics Electrical and Electronics Engineering (JIEEE), Vol. 04, Iss. 01, S No. 007, pp. 1–9, 2023.\u003Cbr>[https://doi.org/10.54060/jieee.v4i1](https://doi.org/10.54060/jieee.v4i1) . |\n| --- |\n| 91\u003Cbr>Received: 04/04/2023\u003Cbr>Accepted: 023/04/2023\u003Cbr>Published: 25/04/2023\u003Cbr>Copyright © 2023 The Author(s) . This work is licensed under the Creative Commons Attribution International License (CC BY 4.0) . [http://creativecommons.org/licens](http://creativecommons.org/licens) |\n| es/by/4 .0/\u003Cbr>  Open Access  |\n\nAbstract  \nThe water quality of river Ganga is an important concern due to its drinking, domestic uses, irrigation and also for aquatic life. But the extent of pollutants in river water has deteriorated the quality of river water. So, the assessment of river water becomes very important. But due to the involved subjectivity and uncertainty in the decision making parameter makes the task very complex. In this study, machine learning and fuzzy techniques are utilized to develop the river water quality assessment models. The quality of the water is grouped into three classes. Four machine learning algorithms namely decision tree, random forest tree, k-nearest neighbor and support vector machine are used and implemented on python and anaconda platform. Whereas, three fuzzy based models (fuzzy decision tree, wang-mendel and fast prototyping) are developed using Guaje open source software. All the seven models are analyzed in terms of accuracy, precision, recall and f1-score. The observed result shows that the fuzzy decision tree-based assessment model performs more accurately as compared with the machine learning based models.  \nKeywords  \nRiver water model, machine learning, fuzzy system, Ganga River  \n1. Introduction  \nRiver water is the one of the most vital resources for all kinds of life. It is playing important role for sustaining a good health in human life and also for the growth of nation’s economy. However, it is in persistent danger of pollution by life itself. Industrial wastes, marine dumping, radioactive waste, atmospheric deposition, domestic discharge and many more are the  \nreasons which led to the deterioration of water quality to an extreme level. The consumption of poor quality water in daily life is a major factor for the increase in diseases like Cholera, Diarrhoea, Malaria, Typhoid, and Filariasis [1] . The poor water quality also causes a GDP loss of country every year [2] .  \nConsidering the above horrific consequences, it becomes very necessary to assess the quality of water. At present, the quality of river water is assessed through an expensive and time-consuming process since it includes sample collection, time to move the sample to labs and a considerable amount of time taken for experiments and statistical analysis. In this regard to overcome this inefficient approach, a model based on machine learning (ML) and fuzzy logic has been implemented and studied for the assessment of water quality in real time. The assessment of water quality is particularly uncertain due to the constant change in the values of decision parameters [4] . The types of water quality parameters are summarized in Table 1.  \nTable 1. Classification of water quality parameters [5] .  \n\n| S. No. | Chemical Parameters |\n| --- | --- |\n| 1 | pH |\n| 2 | ","cbCaicHACJmbTmfV","https://ap.wps.com/l/cbCaicHACJmbTmfV","pdf",354153,1,9,"English","en",105,"# Abstract\n# Introduction\n## Water quality importance and pollution sources\n## Limitations of traditional assessment\n## Proposed ML and fuzzy approach\n## Selected decision parameters\n# Related work","[{\"question\":\"How is the river water quality classified in this study?\",\"answer\":\"The quality of river water is grouped into three classes to support model-based assessment.\"},{\"question\":\"Which machine learning and fuzzy models are used?\",\"answer\":\"Four machine learning algorithms are tested: decision tree, random forest, k-nearest neighbor, and support vector machine. Three fuzzy-based models are also developed: fuzzy decision tree, Wang-Mendel, and fast prototyping.\"},{\"question\":\"What evaluation metrics are used to compare the models?\",\"answer\":\"Models are analyzed using accuracy, precision, recall, and F1-score to quantify performance and reliability.\"}]","Assessment of Water Quality using Machine Learning and Fuzzy Techniques | PDF",1785680371,23,{"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},"assessment-of-water-quality-using-machine-learning-and-fuzzy-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/assessment-of-water-quality-using-machine-learning-and-fuzzy-techniques/117917/",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},"How is the river water quality classified in this study?","Question",{"text":75,"@type":76},"The quality of river water is grouped into three classes to support model-based assessment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning and fuzzy models are used?",{"text":80,"@type":76},"Four machine learning algorithms are tested: decision tree, random forest, k-nearest neighbor, and support vector machine. Three fuzzy-based models are also developed: fuzzy decision tree, Wang-Mendel, and fast prototyping.",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation metrics are used to compare the models?",{"text":84,"@type":76},"Models are analyzed using accuracy, precision, recall, and F1-score to quantify performance and reliability.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]