[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125968-en":3,"doc-seo-125968-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125968,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Optimizing water quality classification using random forest and machine learning","Water is a critical natural resource whose quality has been increasingly affected by industrialization and human activities. Effective water quality monitoring is therefore essential for cities worldwide, and modern tools such as cloud computing, AI, remote sensing, and IoT enable more automated assessment. This study applies a random forest model to classify water quality using chemical attributes and runs three experiments with full features, excluding pH, and using only the three most significant features. Full and top-3 feature sets reach 100% accuracy, while removing pH reduces accuracy to 76%, showing both pH importance and compensation by other variables.","Optimizing water quality classification using random forest and machine learning  \nVladislav Kukartsev1,2, Vasiliy Orlov2*, Evgenia Semenova2, and Alyona Rozhkova3  \n1Reshetnev Siberian State of Science and Technology, Krasnoyarsk, Russia  \n2Bauman Moscow State Technical University, Artificial Intelligence Technology Scientific and Education Center, Moscow, Russia  \n3Agriculture Krasnoyarsk state agrarian university, Krasnoyarsk, Russia  \nAbstract. Water is the most precious and essential resource among all natural resources. With the increase in industrialization and human activities over recent decades, the state of water resources has been significantly impacted. Effective water quality monitoring has become a priority for cities worldwide. Modern technologies such as cloud computing, artificial intelligence, remote sensing, and the Internet of Things provide new opportunities to enhance water resource monitoring systems. This paper explores the application of the random forest model for water quality classification based on chemical attributes. The study includes three experiments: using the full set of features, excluding the pH feature, and using only the top three significant features. The random forest model trained on the full dataset achieved 100% accuracy. When the pH feature was excluded, the model maintained an accuracy of 76%, highlighting the importance of this feature but also showing the potential for compensation by other parameters. Using only the top three significant features (pH, conductivity, and nitrate), the model again achieved 100% accuracy. The results demonstrate that feature optimization without significant loss of model accuracy is a promising approach to improve water quality monitoring and assessment processes. This approach allows for reduced data collection time and costs while maintaining high predictive accuracy. The findings confirm that machine learning, particularly random forest models, can be effectively used for water quality classification, ultimately supporting  \nbetter management and conservation of water resources.  \n1 Introduction  \nAccess to good-quality drinking water is a fundamental requirement for ensuring public health and safety. The consequences of poor-quality water can cause severe health-related problems, including gastrointestinal diseases, neurological issues, and even death. Most accepted methods to assess water quality are chemical analyses, which must be undertaken in specially equipped laboratories; these analyses are accurate but often time- and costconsuming, needing specialized equipment and expert personnel [1-3] .  \n* [Corresponding author:](Corresponding author: vasi4244@gmail.com)[ ](Corresponding author: vasi4244@gmail.com)[vasi4244@gmail.com](Corresponding author: vasi4244@gmail.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nThis is based on the growing integration of modern technologies such as cloud computing, artificial intelligence, remote sensing, big data, and the Internet of Things into the development of innovative methods aimed at improving and automating the assessment of water quality. Particularly, machine learning models have shown great potential through predictive analysis of water quality variables from large data sets. It reduces the traditional methods both in time and cost, which is also scalable for monitoring water quality continuously [4-5] .  \nThe following research application is classified into the quality of water through machine learning techniques in various chemical attributes and measurement collected from rivers, dams, and lakes across the length and breadth of India. Aquaattributes encompassing measurements like temperature, D.O (dissolved oxygen), pH, conductivity, B.O.D (biochemical oxygen demand), nitrate, fecal coli","cbCaibpXAZlw8r8d","https://ap.wps.com/l/cbCaibpXAZlw8r8d","pdf",2177307,2,1,10,"English","en",105,"# Abstract\n# Introduction\n# Materials and methods\n## Data Sources","[{\"question\":\"Why is water quality monitoring considered a priority for cities worldwide?\",\"answer\":\"Growing industrialization and human activity have significantly impacted water resources. Monitoring helps manage water quality to protect public health and safety.\"},{\"question\":\"What model is used for water quality classification in this study?\",\"answer\":\"The study uses a random forest model trained on chemical attributes of water samples.\"},{\"question\":\"How does feature optimization affect classification accuracy?\",\"answer\":\"Using all features achieves 100% accuracy; excluding pH reduces accuracy to 76%; using only the top three significant features (pH, conductivity, nitrate) restores 100% accuracy, indicating optimization can reduce data cost with minimal performance loss.\"}]","Optimizing water quality classification using random forest and machine learning | PDF",1785902287,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"optimizing-water-quality-classification-using-random-forest-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/optimizing-water-quality-classification-using-random-forest-and-machine-learning/125968/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is water quality monitoring considered a priority for cities worldwide?","Question",{"text":76,"@type":77},"Growing industrialization and human activity have significantly impacted water resources. Monitoring helps manage water quality to protect public health and safety.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What model is used for water quality classification in this study?",{"text":81,"@type":77},"The study uses a random forest model trained on chemical attributes of water samples.",{"name":83,"@type":74,"acceptedAnswer":84},"How does feature optimization affect classification accuracy?",{"text":85,"@type":77},"Using all features achieves 100% accuracy; excluding pH reduces accuracy to 76%; using only the top three significant features (pH, conductivity, nitrate) restores 100% accuracy, indicating optimization can reduce data cost with minimal performance loss.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":22,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]