Modeling potential arsenic enrichment and distribution using stacking ensemble learning in the lower Yellow River Plain, China

Abstract

In the high arsenic area of the northern Henan Plain in the lower Yellow River, the interaction between surface water and groundwater is frequent due to the control of groundwater over exploitation and the ecological replenishment of the rivers. It is not clear that what leads to the cause and dynamic change in the mechanism of arsenic pollution in shallow groundwater. Using machine learning algorithms to model the risk of high arsenic occurrence in the groundwater can help analyze mechanism of arsenic in groundwater. In this study, a stacking ensemble learning model was constructed to predict the risk distribution of shallow high arsenic groundwater in the lower reaches of the Yellow River in northern Henan Province using multi-variate parameters. Furthermore, we analyzed the potential areas at risk of high arsenic enrichment under different groundwater levels by control. The results show that the groundwater arsenic exceedance rate was 16.76%, and the high arsenic groundwater distributed from northeast to southwest, while that in the middle and south mainly distributed in the alluvial pre-fan depressions and the Yellow River crevasse splay. Compared with the single model, the stacking ensemble learning model has the Area Under the Curve (0.87), accuracy (0.82), and specificity (0.88) and sensitivity (0.77). With the best overall performance, the predicted risk distribution of groundwater arsenic is highly consistent with the observed results, and the potential groundwater area with high arsenic risk accounts for 19.67% of the total area. The impact of the Yellow River burst, average annual temperature, annual precipitation, ground elevation, and hydraulic gradient as the most significant indicator factors are affecting groundwater arsenic enrichment in the study area. Among these factors, the sedimentary environment accounts for 27.10% in the process of arsenic enrichment. Groundwater also plays an important role, with a relative share of 13.69%. In the simulation of groundwater rebound process, it was found that when the groundwater level raised in the range of 1 m −3 m, the high arsenic area could be reduced by up to 19.52%. The methods and findings in this work can support the management and control of arsenic enrichment and distribution in shallow groundwater systems around the world.