A novel hybrid random forest linear model approach for forecasting groundwater fluoride contamination

• Published in: Environmental science and pollution research international Vol. 30; no. 17; pp. 50661 - 50674
• Main Authors: Nafouanti, Mouigni Baraka, Li, Junxia, Nyakilla, Edwin E., Mwakipunda, Grant Charles, Mulashani, Alvin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023; Springer Nature B.V
• Subjects: Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; basins; Contamination; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental Monitoring - methods; Environmental science; Fluorides; Fluorides - analysis; Forecasting; Groundwater; Groundwater - analysis; groundwater contamination; Groundwater quality; hybrids; Linear Models; Mathematical models; prediction; Random Forest; Research Article; Subsurface water; Waste Water Technology; Water analysis; Water Management; Water pollution; Water Pollution Control; Water quality; Water sampling; Hybrid random forest linear model; Fluoride; Extreme gradient boosting; Groundwater; Light gradient boosting machine

Groundwater quality in the Datong basin is threatened by high fluoride contamination. Laboratory analysis is a standard method for estimating groundwater quality parameters, which is expensive and time-consuming. Therefore, this paper proposes a hybrid random forest linear model (HRFLM) as a novel a...