교육데이터 활용•지원 서비스

The ascii grids represent regional probabilities that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL) for water quality. The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 meters (m). We utilized prediction modeling methods, specifically boosted regression trees (BRT) with a Bernoulli error distribution within a statistical learning framework within R's computing framework (http://www.r-project.org/) to produce two-dimensional probability grids at selected depths throughout the modeling domain. The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2,767 wells within the alluvial boundary of the Central Valley and over 60 predictor variables from 7 sources (see metadata) and were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. The wells used in the BRT models described above were attributed to predictor variable values in ArcGIS using a 500-m buffer. The response variable data consisted of measured DO and Mn concentrations from 2,767 wells within the alluvial boundary of the Central Valley. The data were compiled from two sources: U.S. Geological Survey (USGS) National Water Information System (NWIS) database (all data are publicly available from the USGS at http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water-quality data are publicly available from the SWRCB at http://geotracker.waterboards.ca.gov/gama/). Only wells with well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 that had the required water-quality data was used. Data were available for 932 wells for the NWIS dataset and 1,835 wells for the SWRCB-DDW dataset. Models were trained on a USGS NWIS dataset of 932 wells and evaluated on an independent hold-out dataset of 1,835 wells from the SWRCB-DDW. We used cross-validation to assess the predictive performance of models of varying complexity as a basis for selecting the final models used to create the prediction grids. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa, accuracy, and the area under the receiver operator characteristic (ROC) curve. The final trained models were used for mapping predictions at discrete depths to a depth of approximately 300 m. Trained DO and Mn models had accuracies of 86–100 percent, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95 percent, and ROC values were 0.87–0.91, indicating good predictive performance. Kappa values for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97 percent, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The