Predicting the spatial distribution of phosphorus concentration in Quaternary sedimentary aquifers using simple field parameters

Geogenic phosphorus (P) in groundwater has been found in different regions, posing a risk for surface water eutrophication. However, the prediction of groundwater P distribution is less studied. In this study, three machine learning-based regression models including random forest regression (RFR), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the spatial distribution of geogenic P in alluvial-lacustrine sedimentary aquifers of the central Yangtze River basin, with readily accessible field parameters, such as pH, Eh, EC, depth, NH4+-N and Fe2+. The results indicate that the RFR model achieves the best fit by three times 10-fold cross-validation with the highest R-2 (0.49) and explanatory variance (0.52), the lowest root mean square error (0.48) and mean absolute error (0.30), producing a groundwater P distribution that is highly consistent with the observed results. According to the prediction results, the areas with high P (> 0.4 mg/L) groundwater and abnormally high P (> 1 mg/L) groundwater account for 55% and 11% of the whole study area in JH-DT-P, respectively. Meanwhile, NH4+-N and Fe2+ are the two most prominent indicating factors of P enrichment in groundwater, and NH4+-N has higher relative importance than Fe2+. The wider validity of the model was suggested by its successful application to two regions in Bangladesh with similar hydrogeological conditions.

Automated summary

This study predicted the spatial distribution of geogenic phosphorus in groundwater using machine learning-based regression models in the central Yangtze River basin. The results showed that the random forest regression (RFR) model achieved the best fit and had a high consistency with the observed results. NH4+-N and Fe2+ were identified as important indicators of phosphorus enrichment in groundwater.