Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models

Wenjing Guo; Zhipeng Gao; Huaming Guo; Wengeng Cao

doi:10.1016/j.scitotenv.2023.165511

Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models

Sci Total Environ. 2023 Nov 1:897:165511. doi: 10.1016/j.scitotenv.2023.165511. Epub 2023 Jul 12.

Authors

Wenjing Guo¹, Zhipeng Gao², Huaming Guo³, Wengeng Cao⁴

Affiliations

¹ State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, PR China; MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, PR China.
² State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, PR China; MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, PR China. Electronic address: zpgao@cugb.edu.cn.
³ State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, PR China; MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, PR China. Electronic address: hmguo@cugb.edu.cn.
⁴ Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, PR China.

PMID: 37442467
DOI: 10.1016/j.scitotenv.2023.165511

Abstract

The relative importance of groundwater geochemicals and sediment characteristics in predicting groundwater arsenic distributions was rarely documented. To figure this out, we established a random forest machine-learning model to predict groundwater arsenic distributions in the Hetao Basin, China, by using 22 variables of climate, topographic features, soil properties, sediment characteristics, groundwater geochemicals, and hydraulic gradients of 492 groundwater samples. The established model precisely captured the patchy distributions of groundwater arsenic concentrations in the basin with an AUC value of 0.84. Results suggest that Fe(II) was the most prominent variable in predicting groundwater arsenic concentrations, which supported that the enrichment of arsenic in groundwater was caused by the reductive dissolution of Fe(III) oxides. The high relative importance of SO₄^2- indicated that sulfate reduction was also conducive to groundwater arsenic enrichment in inland basins. Nevertheless, parameters of climate variables, sediment characteristics, and soil properties showed secondly important roles in predicting groundwater arsenic concentrations. The other two models, which excluded parameters of groundwater geochemicals and/or sediment characteristics, showed much worse predictions than the model considering all variables. This highlights the importance of variables of groundwater geochemicals and sediment characteristics in improving the precision and accuracy of predicting results. Future studies should probe a method constructing the random forest predicting model with high precision based on the limited number of groundwater samples and sediment samples.

Keywords: Arsenic; Groundwater; Hetao Basin; Prediction; Random forest model.