Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States
Groundwater is an important source of drinking water supplies in the conterminous United State (CONUS), and presence of high nitrate concentrations may limit usability of groundwater in some areas because of the potential negative health effects. Prediction of locations of high nitrate groundwater is needed to focus mitigation and relief efforts. A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate. Nitrate was predicted at a 1 km resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrations from 12,082 wells and included predictor variables representing well characteristics, hydrologic conditions, soil type, geology, land use, climate, and nitrogen inputs. Predictor variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. The model provided accurate estimates at national and regional scales: the training (R2 of 0.83) and hold-out (R2 of 0.49) data fits compared favorably to previous studies. Predicted nitrate concentrations were less than 1 mg/L across most of the CONUS. Nationally, well depth, soil and climate characteristics, and the absence of developed land use were among the most influential explanatory factors. Only 1% of the area in either water supply zone had predicted nitrate concentrations greater than 10 mg/L; however, about 1.4 M people depend on groundwater for their drinking supplies in those areas. Predicted high concentrations of nitrate were most prevalent in the central CONUS. In areas of predicted high nitrate concentration, applied manure, farm fertilizer, and agricultural land use were influential predictor variables. This work represents the first application of XGB to a three-dimensional national-scale groundwater quality model and provides a significant milestone in the efforts to document nitrate in groundwater across the CONUS.
Citation Information
| Publication Year | 2021 |
|---|---|
| Title | Machine learning predictions of nitrate in groundwater used for drinking supply in the conterminous United States |
| DOI | 10.1016/j.scitotenv.2021.151065 |
| Authors | Katherine Marie Ransom, Bernard T. Nolan, Paul Stackelberg, Kenneth Belitz, Miranda S. Fram |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Science of the Total Environment |
| Index ID | 70225673 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | California Water Science Center |
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Data for Machine Learning Predictions of Nitrate in Groundwater Used for Drinking Supply in the Conterminous United States
A three-dimensional extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution for two drinking water zones, each of variable depth, one for domestic supply and one for public supply. The model used measured nitrate concentrati - Connect
Miranda Fram
Program Chief, Statewide and National Water Quality AssessmentsEmailPhone