{"title":"用于水质预测的元启发式优化算法(Lion BES XGB)","authors":"Kalaivanan K, Vellingiri J","doi":"10.1080/1573062X.2023.2209558","DOIUrl":null,"url":null,"abstract":"ABSTRACT Recently, water contamination has become a major problem in developing countries due to urbanization and population growth, leading to an increase in morbidity and mortality rates.Therefore, accurate water quality prediction is crucial in the urban water supply system. In this work, we developed a prediction model based on Extreme Gradient Boosting (XGB) using a hybrid feature selection approach combining Lion Swarm Optimization (LSO) and Bald Eagle Search (BES). The proposed method LSO-BES-XGB consists of three steps: preprocessing, feature selection, and classification.Z-score normalization helps fill in missing data values by scaling to indicate the number of standard deviations from the mean. LSO-BES Feature selection identifies relevant features, and the XGB classifier determines whether the water is normal or contaminated. The LSO-BES-XGB model was applied to the Cauvery River data set and achieved 94.22% accuracy, 93.12% precision, 94.23% recall, and 92.45%.","PeriodicalId":49392,"journal":{"name":"Urban Water Journal","volume":"20 1","pages":"751 - 762"},"PeriodicalIF":1.6000,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A meta heuristic optimization algorithm (Lion-BES-XGB) for water quality prediction\",\"authors\":\"Kalaivanan K, Vellingiri J\",\"doi\":\"10.1080/1573062X.2023.2209558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Recently, water contamination has become a major problem in developing countries due to urbanization and population growth, leading to an increase in morbidity and mortality rates.Therefore, accurate water quality prediction is crucial in the urban water supply system. In this work, we developed a prediction model based on Extreme Gradient Boosting (XGB) using a hybrid feature selection approach combining Lion Swarm Optimization (LSO) and Bald Eagle Search (BES). The proposed method LSO-BES-XGB consists of three steps: preprocessing, feature selection, and classification.Z-score normalization helps fill in missing data values by scaling to indicate the number of standard deviations from the mean. LSO-BES Feature selection identifies relevant features, and the XGB classifier determines whether the water is normal or contaminated. The LSO-BES-XGB model was applied to the Cauvery River data set and achieved 94.22% accuracy, 93.12% precision, 94.23% recall, and 92.45%.\",\"PeriodicalId\":49392,\"journal\":{\"name\":\"Urban Water Journal\",\"volume\":\"20 1\",\"pages\":\"751 - 762\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Urban Water Journal\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1080/1573062X.2023.2209558\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Urban Water Journal","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1080/1573062X.2023.2209558","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"WATER RESOURCES","Score":null,"Total":0}
A meta heuristic optimization algorithm (Lion-BES-XGB) for water quality prediction
ABSTRACT Recently, water contamination has become a major problem in developing countries due to urbanization and population growth, leading to an increase in morbidity and mortality rates.Therefore, accurate water quality prediction is crucial in the urban water supply system. In this work, we developed a prediction model based on Extreme Gradient Boosting (XGB) using a hybrid feature selection approach combining Lion Swarm Optimization (LSO) and Bald Eagle Search (BES). The proposed method LSO-BES-XGB consists of three steps: preprocessing, feature selection, and classification.Z-score normalization helps fill in missing data values by scaling to indicate the number of standard deviations from the mean. LSO-BES Feature selection identifies relevant features, and the XGB classifier determines whether the water is normal or contaminated. The LSO-BES-XGB model was applied to the Cauvery River data set and achieved 94.22% accuracy, 93.12% precision, 94.23% recall, and 92.45%.
期刊介绍:
Urban Water Journal provides a forum for the research and professional communities dealing with water systems in the urban environment, directly contributing to the furtherance of sustainable development. Particular emphasis is placed on the analysis of interrelationships and interactions between the individual water systems, urban water bodies and the wider environment. The Journal encourages the adoption of an integrated approach, and system''s thinking to solve the numerous problems associated with sustainable urban water management.
Urban Water Journal focuses on the water-related infrastructure in the city: namely potable water supply, treatment and distribution; wastewater collection, treatment and management, and environmental return; storm drainage and urban flood management. Specific topics of interest include:
network design, optimisation, management, operation and rehabilitation;
novel treatment processes for water and wastewater, resource recovery, treatment plant design and optimisation as well as treatment plants as part of the integrated urban water system;
demand management and water efficiency, water recycling and source control;
stormwater management, urban flood risk quantification and management;
monitoring, utilisation and management of urban water bodies including groundwater;
water-sensitive planning and design (including analysis of interactions of the urban water cycle with city planning and green infrastructure);
resilience of the urban water system, long term scenarios to manage uncertainty, system stress testing;
data needs, smart metering and sensors, advanced data analytics for knowledge discovery, quantification and management of uncertainty, smart technologies for urban water systems;
decision-support and informatic tools;...