比例(比率)
地下水
环境科学
质量(理念)
水质
水文学(农业)
计算机科学
水资源管理
环境资源管理
工程类
地理
地图学
岩土工程
生态学
生物
认识论
哲学
作者
Abhimanyu Singh Yadav,Abhay Raj,Basant Yadav
标识
DOI:10.1016/j.jenvman.2024.122903
摘要
Assessing groundwater quality typically involves labor-intensive, time-consuming, and costly laboratory tests, making real-time monitoring impractical, especially at the local level. Groundwater quality projections at the local scale using broad spatial datasets have been inaccurate due to variations in hydrogeology, human activities, industrial operations, groundwater extraction, and waste disposal. This study aims to identify the most dependable and resilient machine learning algorithms for forecasting groundwater quality at nearby monitoring locations by utilizing simple water quality metrics that can be quickly assessed without extensive sampling and laboratory testing. The Entropy-weighted Water Quality Index (EWQI) was calculated using a large spatial and temporal dataset (2014-2021) of 977 wells with parameters including pH, total hardness (TH), calcium (Ca
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