[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117534-en":3,"doc-seo-117534-105":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},117534,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Forecasting Water Quality Through Machine Learning and Hyperparameter Optimization - Research Article","Forecasting water quality through machine learning and hyperparameter optimization strengthens prediction accuracy for environmental monitoring. The study applies a classic machine learning workflow, focusing mainly on XGBoost while benchmarking alternative models including random forest, decision tree, AdaBoost, support vector machine, Naïve Bayes, and extra tree. Performance is assessed with classification reports covering precision, recall, F1-score, and accuracy. The optimized XGBoost model achieves 97.06% accuracy, with 94.22% precision, 81.5% recall, and 87.4% F1-score, indicating a clear improvement over prior approaches.","Forecasting water quality through machine learning and hyperparameter optimization  \nElvin, Antoni Wibowo  \nDepartment of Computer Science, Binus Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia  \nArticle Info ABSTRACT  \n\n| Article history:\u003Cbr>Received Aug 18, 2023 Revised Oct 25, 2023 Accepted Nov 1, 2023 | Forecasting water quality through machine learning and hyperparameter optimization is a research endeavor aimed at enhancing the water quality prediction process. The primary goal of this study is to employ various machine learning algorithms for water quality prediction and to refine existing models from previous research. The paper encompasses a comprehensive literature review of previous water quality prediction studies and introduces novel theoretical insights. The research employs a classic machine learning problem-solving approach, predominantly utilizing the extreme gradient boost (XGBoost) algorithm. Additionally, it evaluates other machine learning algorithms, including the random forest (RF) classifier, decision tree (DT) classifier, adaptive boosting (AdaBoost) classifier, support vector machine (SVM), Naïve Bayes, and extra tree classifier for comparison. The evaluation process utilizes a classification report, providing insights into the precision, recall, f1-score, and accuracy of each machine learning model. Notably, the XGBoost model exhibits superior performance, achieving an impressive 97.06% accuracy. Precision stands at 94.22%, recall at 81.5%, and F1-score at 87.4% . These results represent a significant advancement over prior water quality prediction models, emphasizing the potential of machine learning and hyperparameter optimization to enhance water quality forecasting in environmental monitoring.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Classification report Hyperparameter tuning Machine learning Python\u003Cbr>Water quality |  |\n\nCorresponding Author:  \nElvin  \nDepartment of Computer Science, Binus Graduate Program-Master of Computer Science Bina Nusantara University  \nJakarta, Indonesia  \nEmail: [elvin005@binus.ac.id](elvin005@binus.ac.id)  \n1. INTRODUCTION  \nWater is an inorganic, transparent, and colorless chemical substance that is required for the survival of most existing organisms and humans. Adequate water quality is an absolute necessity for the sustenance of all living beings. Aquatic species possess a finite tolerance for pollution, and surpassing these limits imperils their very existence. To maintain a dependable and safe water supply, constant vigilance through water quality monitoring is imperative. With the growth of our economy and the expansion of urban areas, water contamination has surged in significance. The intricate task of predicting factors that influence water quality within hydrophyte systems remains a challenging endeavor. The exploration of diverse methodologies to forecast water quality in reservoirs bears profound implications both in theory and practicality [1], [2] . Water that has poor quality will result in health and safety conditions for living things. Contaminated drinking water not only poses significant health risks but also exerts adverse effects on the environment and infrastructure, with the quality of water being compromised due to a combination of factors such as  \ninadequate infrastructure, lack of public awareness, and poor hygiene standards. Based on extensive research conducted by the United Nations, it has been revealed that approximately 1.5 million lives are tragically lost each year due to water-borne diseases. This distressing statistic underscores the urgent need for global efforts to ensure access to clean and safe drinking water for all, as well as the importance of sanitation and hygiene practices in preventing such devastating consequences. Addressing this issue remains a critical imperative on the global agenda, as we strive to safeguard the healt","cbCaijkESYXHXtvl","https://ap.wps.com/l/cbCaijkESYXHXtvl","pdf",655828,1,11,"English","en",105,"# Introduction\n## Motivation for Water Quality Monitoring\n## Water Quality Assessment Using Machine Learning\n## Research Objectives and Contributions","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To improve water quality prediction by applying multiple machine learning algorithms and refining models using hyperparameter optimization.\"},{\"question\":\"Which model performs best in the evaluation?\",\"answer\":\"The XGBoost model shows the strongest results, reaching 97.06% accuracy and high precision, recall, and F1-score values.\"},{\"question\":\"How is model performance measured?\",\"answer\":\"Performance is measured using a classification report that reports precision, recall, F1-score, and accuracy for each machine learning model.\"}]","Forecasting Water Quality Through Machine Learning and Hyperparameter Optimization - Research Article | PDF",1785676762,28,{"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},"forecasting-water-quality-through-machine-learning-and-hyperparameter-optimization-research-article","",{"@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/forecasting-water-quality-through-machine-learning-and-hyperparameter-optimization-research-article/117534/",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 objective of the study?","Question",{"text":75,"@type":76},"To improve water quality prediction by applying multiple machine learning algorithms and refining models using hyperparameter optimization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which model performs best in the evaluation?",{"text":80,"@type":76},"The XGBoost model shows the strongest results, reaching 97.06% accuracy and high precision, recall, and F1-score values.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance measured?",{"text":84,"@type":76},"Performance is measured using a classification report that reports precision, recall, F1-score, and accuracy for each machine learning model.","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"]