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
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-25886-w

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 approach for estimating groundwater fluoride contamination. Light gradient boosting (LightGBM), random forest (RF), and extreme gradient boosting (Xgboost) were also employed in comparison with HRFLM for predicting fluoride contamination in groundwater. 202 groundwater samples were collected to draw up the performance capability of several models in forecasting subsurface water fluoride contamination. The performance of the models was assessed utilizing the receiver operating characteristic (ROC) area under the curve (AUC) and the confusion matrix (CM). The CM results reveal that with nine predictor variables, the hybrid HRFLM achieved an accuracy of 95%, outperforming the Xgboost, LightGBM, and RF models, which attained 88%, 88%, and 85%, respectively. Likewise, the AUC results of the hybrid HRFLM show high performance with an AUC of 0.98 compared to Xgboost, LightGBM, and RF, which achieved an AUC of 0.95, 0.90, and 0.88, respectively. The study demonstrates that the HRFLM can be applied as an advanced approach for groundwater fluoride contamination prediction in the Datong basin and could be adopted in various areas facing a similar challenge.