{"title":"利用机器学习技术预测水质","authors":"Er. P Nagalakshmi, Dr.P.Ganesh Kumar","doi":"10.55041/ijsrem36721","DOIUrl":null,"url":null,"abstract":"The quality of water is a critical parameter that affects human health, aquatic ecosystems, and environmental sustainability. The prediction of water quality using machine learning techniques has emerged as a promising solution for early detection and management of water pollution. This project focuses on developing a predictive model that leverages historical water quality data to forecast future water quality indices. Various machine learning algorithms, including regression and classification techniques, will be employed to analyze parameters such as pH, turbidity, dissolved oxygen, and contaminant levels. By training the model on a comprehensive dataset, the system aims to provide accurate and timely predictions, enabling proactive measures to be taken to ensure safe water supplies. The implementation of this model can significantly aid regulatory bodies and water management authorities in monitoring and maintaining water quality standards, ultimately contributing to public health and environmental conservation.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"69 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WATER QUALITY PREDICTION USING MACHINE LEARNING TECHNIQUE\",\"authors\":\"Er. P Nagalakshmi, Dr.P.Ganesh Kumar\",\"doi\":\"10.55041/ijsrem36721\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The quality of water is a critical parameter that affects human health, aquatic ecosystems, and environmental sustainability. The prediction of water quality using machine learning techniques has emerged as a promising solution for early detection and management of water pollution. This project focuses on developing a predictive model that leverages historical water quality data to forecast future water quality indices. Various machine learning algorithms, including regression and classification techniques, will be employed to analyze parameters such as pH, turbidity, dissolved oxygen, and contaminant levels. By training the model on a comprehensive dataset, the system aims to provide accurate and timely predictions, enabling proactive measures to be taken to ensure safe water supplies. The implementation of this model can significantly aid regulatory bodies and water management authorities in monitoring and maintaining water quality standards, ultimately contributing to public health and environmental conservation.\",\"PeriodicalId\":504501,\"journal\":{\"name\":\"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT\",\"volume\":\"69 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55041/ijsrem36721\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55041/ijsrem36721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
WATER QUALITY PREDICTION USING MACHINE LEARNING TECHNIQUE
The quality of water is a critical parameter that affects human health, aquatic ecosystems, and environmental sustainability. The prediction of water quality using machine learning techniques has emerged as a promising solution for early detection and management of water pollution. This project focuses on developing a predictive model that leverages historical water quality data to forecast future water quality indices. Various machine learning algorithms, including regression and classification techniques, will be employed to analyze parameters such as pH, turbidity, dissolved oxygen, and contaminant levels. By training the model on a comprehensive dataset, the system aims to provide accurate and timely predictions, enabling proactive measures to be taken to ensure safe water supplies. The implementation of this model can significantly aid regulatory bodies and water management authorities in monitoring and maintaining water quality standards, ultimately contributing to public health and environmental conservation.