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Enhancing groundwater vulnerability assessment: Comparative study of three machine learning models and five classification schemes for Cuddalore district.

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第一作者： Saravanan,Subbarayan

第一单位： Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, India. Electronic address: saravanan@nitt.edu.

作者： Saravanan,Subbarayan ^[1] ; Saranya,Thiyagarajan ^[2] ; Shankar,Karuppannan ^[3] ; Balamurugan,Panneerselvam ^[4]

作者单位： Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, India. Electronic address: saravanan@nitt.edu. ^[1] Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, India. Electronic address: drsaranyaphd2205@gmail.com. ^[2] Department of Applied Geology, School of Applied Natural Sciences, Adama Science and Technology University, Adama, Ethiopia; Department of Research Analytics, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India. Electronic address: geoshankar1984@gmail.com. ^[3] Center of Excellence in Interdisciplinary Research for Sustainable Development, Faculty of Engineering, Chulalongkorn University, Thailand. Electronic address: balamurugan.phd10@gmail.com. ^[4]

关键词 Climate change impact Groundwater vulnerability Machine learning Multi-class classification Optimized DRASTIC

医学主题词环境监测(Environmental Monitoring);硝酸盐类(Nitrates);地理信息系统(Geographic Information Systems)

DOI 10.1016/j.envres.2023.117769

PMID 38029825

发布时间 2024-02-02

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