Data used to model and map manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA

Data used to model and map manganese concentrations in groundwater in the Northern Atlantic Coastal Plain (NACP) aquifer system, eastern USA, are documented in this data release. The model predicts manganese concentration within four classes and is based on concentration data from 4492 wells. The well data were compiled from U.S. Geological Survey, U.S. Environmental Protection Agency, Suffolk County Water Authority (Suffolk County, New York), and state agency sources. The four concentration classes are based on guidelines for drinking water quality: below detection (class 1, less than 10 micrograms per liter (ug/L)); detected but less than the aesthetic guideline of 50 ug/L (class 2); greater than the aesthetic...

Categories: Data; Tags: Aquia aquifer, Delaware, Delmarva Peninsula, Geochemistry, Hydrology, All tags... Long Island, Lower Chesapeake aquifer, Magothy aquifer, Maryland, Matawan aquifer, Monmouth - Mt. Laurel aquifer, NAWQA, New Jersey, New York, North Carolina, Northern Atlantic Coastal Plain aquifer system, Piney Point aquifer, Potomac - Patapsco aquifer, Potomac - Patuxent aquifer, Surficial aquifer, USGS Science Data Catalog (SDC), Upper Chesapeake aquifer, Virginia, Water Quality, Water Resources, XGboost, class imbalance, groundwater quality, machine learning, manganese, regional groundwater quality, Fewer tags

Geothermal resource favorability: select features and predictions for the western United States curated for DOI 10.1016/j.geothermics.2023.102662

The data contained herein are five input features (i.e., heat flow, distance to the nearest quaternary fault, distance to the nearest quaternary magma body, seismic event density, maximum horizontal stress) and labels (i.e., where known geothermal systems have been identified) from Williams and DeAngelo (2008) and nine favorability maps from Mordensky et al. (2023). The favorability maps are the untransformed predictions from models resulting from the features and labels used with either the methods presented in Williams and DeAngelo (2008) or the machine learning approaches presented in Mordensky et al. (2023). Each favorability map depicts an estimate of relative favorability with respect to the other locations...

Tags: ANN, Arizona, California, Colorado, Energy Resources, All tags... Idaho, Montana, Nevada, New Mexico, Oregon, SVM, USGS Science Data Catalog (SDC), Utah, Washington, Western United States, Wyoming, XGBoost, artificial neural network, data driven, data-driven, eXtreme Gradient Boosting, feed forward neural network, geothermal, geothermal favorability, geothermal resource assessment, heat flow, logistic regression, machine learning, magmatism, multilayer perceptron neural network, quaternary faults, seismicity, stress, support-vector machine, weight of evidence, weight-of-evidence, Fewer tags

Data and Model Archive for Preliminary Machine Learning Models of Manganese and 1,4-Dioxane in Groundwater on Long Island, New York

Data and preliminary machine-learning models used to predict manganese and 1,4-dioxane in groundwater on Long Island are documented in this data release. Concentration data used to develop the models were from 910 wells for manganese and 553 wells for 1,4-dioxane, primarily public supply wells, from U.S. Geological Survey, U.S. Environmental Protection Agency (USEPA), and Suffolk County Water Authority sources. Thirty-two explanatory variables describe depth, groundwater flow, land use, soil properties, and other features of the aquifer system. The models use XGBoost, an ensemble tree machine learning method. Four models are documented for manganese, predicting the probability of concentrations relative to four...

Tags: 1,4-dioxane, Hydrology, Kings County, Long Island, NAWQA, All tags... Nassau County, New York, Queens County, Suffolk County, USGS Science Data Catalog (SDC), Water Quality, XGboost, groundwater quality, machine learning, manganese, regional groundwater quality, Fewer tags