Tue Duy Nguyen , Quynh Thi Phuong Le , Man Thi Truc Doan , Ha Manh Bui
{"title":"Predicting total alkalinity in saline water using machine learning: A case study with RapidMiner","authors":"Tue Duy Nguyen , Quynh Thi Phuong Le , Man Thi Truc Doan , Ha Manh Bui","doi":"10.1016/j.scowo.2024.100032","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigates the use of machine learning models to predict total alkalinity (TA) based on chloride concentration (Cl<sup>-</sup>), pH and temperature. Utilizing RapidMiner's Auto Mode, six machine learning models were applied to a dataset of 111 water samples from the Nhà Bè River. The models' performances were evaluated using Root Mean Square Error (RMSE) and R² metrics, with the Generalized Linear Model (GLM), Support Vector Machine (SVM) and Deep Learning models identified as the top performers. Correlation and coefficient analyses revealed that Cl<sup>-</sup> had the most significant impact on TA prediction, followed by temperature and pH. These findings underscore the effectiveness of machine learning in water quality monitoring, presenting a cost-effective alternative to traditional chemical analysis methods.</div></div>","PeriodicalId":101197,"journal":{"name":"Sustainable Chemistry One World","volume":"4 ","pages":"Article 100032"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Chemistry One World","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950357424000325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study investigates the use of machine learning models to predict total alkalinity (TA) based on chloride concentration (Cl-), pH and temperature. Utilizing RapidMiner's Auto Mode, six machine learning models were applied to a dataset of 111 water samples from the Nhà Bè River. The models' performances were evaluated using Root Mean Square Error (RMSE) and R² metrics, with the Generalized Linear Model (GLM), Support Vector Machine (SVM) and Deep Learning models identified as the top performers. Correlation and coefficient analyses revealed that Cl- had the most significant impact on TA prediction, followed by temperature and pH. These findings underscore the effectiveness of machine learning in water quality monitoring, presenting a cost-effective alternative to traditional chemical analysis methods.