Katherine Ransom (Former Employee)
Science and Products
Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States
An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution at a depth below the water table of 10 m. The model builds off a previous XGB machine learning model developed to predict nitrate at domestic and public supp
Machine-learning model predictions and rasters of arsenic and manganese in groundwater in the Mississippi River Valley alluvial aquifer
Groundwater from the Mississippi River Valley alluvial aquifer (MRVA) is a vital resource for agriculture and drinking-water supplies in the central United States. Water availability can be limited in some areas of the aquifer by high concentrations of trace elements, including manganese and arsenic. Boosted regression trees, a type of ensemble-tree machine-learning method, were used to predict ma
Random forest regression model and prediction rasters of fluoride in groundwater in basin-fill aquifers of western United States
A random forest regression (RFR) model was developed to predict groundwater fluoride concentrations in four western United Stated principal aquifers - California Coastal basin-fill aquifers, Central Valley aquifer system, Basin and Range basin-fill aquifers, and the Rio Grande aquifer system. The selected basin-fill aquifers are a vital resource for drinking-water supplies. The RFR model was devel
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
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 Cou
A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA
The ascii grids associated with this data release are model inputs representing the Central Valley aquifer, California, and predicted nitrate concentrations (as NO3-N, mg/L) at two depth zones associated with private and public drinking water supply wells, respectively, . The model input and prediction grids are bound by the alluvial bed boundary that defines the Central Valley. The prediction gri
Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning
Study region: The study was conducted in the Northern Atlantic Coastal Plain aquifer system, eastern USA, an important water supply in a densely populated region.Study focus: Manganese (Mn), an emerging health concern and common nuisance contaminant in drinking water, is mapped and modeled using the XGBoost machine learning method, predictions of pH and redox conditions from previous models, and o
Authors
Leslie A. DeSimone, Katherine Marie Ransom
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)
Authors
Katherine Marie Ransom, Bernard T. Nolan, Paul Stackelberg, Kenneth Belitz, Miranda S. Fram
Predicting regional fluoride concentrations at public and domestic supply depths in basin-fill aquifers of the western United States using a random forest model
A random forest regression (RFR) model was applied to over 12,000 wells with measured fluoride (F) concentrations in untreated groundwater to predict F concentrations at depths used for domestic and public supply in basin-fill aquifers of the western United States. The model relied on twenty-two regional-scale environmental and surficial predictor variables selected to represent factors known to c
Authors
Celia Z Rosecrans, Kenneth Belitz, Katherine Marie Ransom, Paul E. Stackelberg, Peter B. McMahon
Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA
The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a l
Authors
Christopher Green, Katherine Marie Ransom, Bernard T. Nolan, Lixia Liao, Thomas Harter
Machine-learning predictions of high arsenic and high manganese at drinking water depths of the glacial aquifer system, northern continental United States
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT mo
Authors
Melinda L. Erickson, Sarah M. Elliott, Craig J. Brown, Paul Stackelberg, Katherine Marie Ransom, James E. Reddy, Charles A. Cravotta
Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States
Groundwater supplies 50% of drinking water worldwide and 30% in the United States. Geogenic and anthropogenic contaminants can, however, compromise water quality, thus limiting groundwater availability. Reduction/oxidation (redox) processes and redox conditions affect groundwater quality by influencing the mobility and transport of common geogenic and anthropogenic contaminants. In the glacial aqu
Authors
Melinda L. Erickson, Sarah M. Elliott, Craig J. Brown, Paul Stackelberg, Katherine Marie Ransom, James E. Reddy
Groundwater quality of aquifers overlying the Oxnard Oil Field, Ventura County, California
Groundwater samples collected from irrigation, monitoring, and municipal supply wells near the Oxnard Oil Field were analyzed for chemical and isotopic tracers to evaluate if thermogenic gas or water from hydrocarbon-bearing formations have mixed with surrounding groundwater. New and historical data show no evidence of water from hydrocarbon-bearing formations in groundwater overlying the field. H
Authors
Celia Z. Rosecrans, Matthew K. Landon, Katherine Marie Ransom, Janice M. Gillespie, Justin T. Kulongoski, Michael J. Stephens, Andrew G. Hunt, David H. Shimabukuro, Tracy Davis
Machine learning predictions of pH in the Glacial Aquifer System, Northern USA
A boosted regression tree model was developed to predict pH conditions in three dimensions throughout the glacial aquifer system of the contiguous United States using pH measurements in samples from 18,386 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and, when
Authors
Paul Stackelberg, Kenneth Belitz, Craig J. Brown, Melinda L. Erickson, Sarah M. Elliott, Leon J. Kauffman, Katherine Marie Ransom, James E. Reddy
Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States
High salinity limits groundwater use in parts of the Mississippi embayment. Machine learning was used to create spatially continuous and three‐dimensional predictions of salinity across drinking‐water aquifers in the embayment. Boosted regression tree (BRT) models, a type of machine learning, were used to predict specific conductance (SC) and chloride (Cl), and total dissolved solids (TDS) was cal
Authors
Katherine J. Knierim, James A. Kingsbury, Connor J. Haugh, Katherine Marie Ransom
Machine-learning models to map pH and redox conditions in groundwater in a layered aquifer system, Northern Atlantic Coastal Plain, eastern USA
Study regionThe study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA.Study focusGroundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex,
Authors
Leslie A. DeSimone, Jason P. Pope, Katherine Marie Ransom
A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA
Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m
Authors
Katherine M. Ransom, Bernard T. Nolan, Jonathan A. Traum, Claudia C. Faunt, Andrew M. Bell, Jo Ann M. Gronberg, David C. Wheeler, Celia Zamora, Bryant C. Jurgens, Gregory E. Schwarz, Kenneth Belitz, Sandra M. Eberts, George Kourakos, Thomas Harter
Non-USGS Publications**
Andy Canion, Katherine M. Ransom, Brian G. Katz; Discrimination of Nitrogen Sources in Karst Spring Contributing Areas Using a Bayesian Isotope Mixing Model and Wastewater Tracers (Florida, USA). Environmental and Engineering Geoscience ; 26 (3): 291–311. doi: https://doi.org/10.2113/EEG-2310
Ransom, K. M., Bell, A. M., Barber, Q. E., Kourakos, G., and Harter, T.: A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California, Hydrol. Earth Syst. Sci., 22, 2739–2758, https://doi.org/10.5194/hess-22-2739-2018, 2018
Willmes M, Ransom KM, Lewis LS, Denney CT, Glessner JJG, Hobbs JA (2018) IsoFishR: An application for reproducible data reduction and analysis of strontium isotope ratios (87Sr/86Sr) obtained via laser-ablation MC-ICP-MS. PLoS ONE 13(9): e0204519. https://doi.org/10.1371/journal.pone.0204519
Ransom, K. M., et al. (2016), Bayesian nitrate source apportionment to individual groundwater wells in the Central Valley by use of elemental and isotopic tracers, Water Resour. Res., 52, 5577– 5597, doi:10.1002/2015WR018523.
Li, X., Atwill, E.R., Antaki, E., Applegate, O., Bergamaschi, B., Bond, R.F., Chase, J., Ransom, K.M., Samuels, W., Watanabe, N. and Harter, T. (2015), Fecal Indicator and Pathogenic Bacteria and Their Antibiotic Resistance in Alluvial Groundwater of an Irrigated Agricultural Region with Dairies. J. Environ. Qual., 44: 1435-1447. https://doi.org/10.2134/jeq2015.03.0139
K.M. Lockhart, A.M. King, T. Harter,
Identifying sources of groundwater nitrate contamination in a large alluvial groundwater basin with highly diversified intensive agricultural production, Journal of Contaminant Hydrology, Volume 151, 2013, Pages 140-154, ISSN 0169-7722, https://doi.org/10.1016/j.jconhyd.2013.05.008.
Identifying sources of groundwater nitrate contamination in a large alluvial groundwater basin with highly diversified intensive agricultural production, Journal of Contaminant Hydrology, Volume 151, 2013, Pages 140-154, ISSN 0169-7722, https://doi.org/10.1016/j.jconhyd.2013.05.008.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States
An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution at a depth below the water table of 10 m. The model builds off a previous XGB machine learning model developed to predict nitrate at domestic and public supp
Machine-learning model predictions and rasters of arsenic and manganese in groundwater in the Mississippi River Valley alluvial aquifer
Groundwater from the Mississippi River Valley alluvial aquifer (MRVA) is a vital resource for agriculture and drinking-water supplies in the central United States. Water availability can be limited in some areas of the aquifer by high concentrations of trace elements, including manganese and arsenic. Boosted regression trees, a type of ensemble-tree machine-learning method, were used to predict ma
Random forest regression model and prediction rasters of fluoride in groundwater in basin-fill aquifers of western United States
A random forest regression (RFR) model was developed to predict groundwater fluoride concentrations in four western United Stated principal aquifers - California Coastal basin-fill aquifers, Central Valley aquifer system, Basin and Range basin-fill aquifers, and the Rio Grande aquifer system. The selected basin-fill aquifers are a vital resource for drinking-water supplies. The RFR model was devel
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
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 Cou
A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA
The ascii grids associated with this data release are model inputs representing the Central Valley aquifer, California, and predicted nitrate concentrations (as NO3-N, mg/L) at two depth zones associated with private and public drinking water supply wells, respectively, . The model input and prediction grids are bound by the alluvial bed boundary that defines the Central Valley. The prediction gri
Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning
Study region: The study was conducted in the Northern Atlantic Coastal Plain aquifer system, eastern USA, an important water supply in a densely populated region.Study focus: Manganese (Mn), an emerging health concern and common nuisance contaminant in drinking water, is mapped and modeled using the XGBoost machine learning method, predictions of pH and redox conditions from previous models, and o
Authors
Leslie A. DeSimone, Katherine Marie Ransom
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)
Authors
Katherine Marie Ransom, Bernard T. Nolan, Paul Stackelberg, Kenneth Belitz, Miranda S. Fram
Predicting regional fluoride concentrations at public and domestic supply depths in basin-fill aquifers of the western United States using a random forest model
A random forest regression (RFR) model was applied to over 12,000 wells with measured fluoride (F) concentrations in untreated groundwater to predict F concentrations at depths used for domestic and public supply in basin-fill aquifers of the western United States. The model relied on twenty-two regional-scale environmental and surficial predictor variables selected to represent factors known to c
Authors
Celia Z Rosecrans, Kenneth Belitz, Katherine Marie Ransom, Paul E. Stackelberg, Peter B. McMahon
Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA
The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a l
Authors
Christopher Green, Katherine Marie Ransom, Bernard T. Nolan, Lixia Liao, Thomas Harter
Machine-learning predictions of high arsenic and high manganese at drinking water depths of the glacial aquifer system, northern continental United States
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT mo
Authors
Melinda L. Erickson, Sarah M. Elliott, Craig J. Brown, Paul Stackelberg, Katherine Marie Ransom, James E. Reddy, Charles A. Cravotta
Machine learning predicted redox conditions in the glacial aquifer system, northern continental United States
Groundwater supplies 50% of drinking water worldwide and 30% in the United States. Geogenic and anthropogenic contaminants can, however, compromise water quality, thus limiting groundwater availability. Reduction/oxidation (redox) processes and redox conditions affect groundwater quality by influencing the mobility and transport of common geogenic and anthropogenic contaminants. In the glacial aqu
Authors
Melinda L. Erickson, Sarah M. Elliott, Craig J. Brown, Paul Stackelberg, Katherine Marie Ransom, James E. Reddy
Groundwater quality of aquifers overlying the Oxnard Oil Field, Ventura County, California
Groundwater samples collected from irrigation, monitoring, and municipal supply wells near the Oxnard Oil Field were analyzed for chemical and isotopic tracers to evaluate if thermogenic gas or water from hydrocarbon-bearing formations have mixed with surrounding groundwater. New and historical data show no evidence of water from hydrocarbon-bearing formations in groundwater overlying the field. H
Authors
Celia Z. Rosecrans, Matthew K. Landon, Katherine Marie Ransom, Janice M. Gillespie, Justin T. Kulongoski, Michael J. Stephens, Andrew G. Hunt, David H. Shimabukuro, Tracy Davis
Machine learning predictions of pH in the Glacial Aquifer System, Northern USA
A boosted regression tree model was developed to predict pH conditions in three dimensions throughout the glacial aquifer system of the contiguous United States using pH measurements in samples from 18,386 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and, when
Authors
Paul Stackelberg, Kenneth Belitz, Craig J. Brown, Melinda L. Erickson, Sarah M. Elliott, Leon J. Kauffman, Katherine Marie Ransom, James E. Reddy
Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States
High salinity limits groundwater use in parts of the Mississippi embayment. Machine learning was used to create spatially continuous and three‐dimensional predictions of salinity across drinking‐water aquifers in the embayment. Boosted regression tree (BRT) models, a type of machine learning, were used to predict specific conductance (SC) and chloride (Cl), and total dissolved solids (TDS) was cal
Authors
Katherine J. Knierim, James A. Kingsbury, Connor J. Haugh, Katherine Marie Ransom
Machine-learning models to map pH and redox conditions in groundwater in a layered aquifer system, Northern Atlantic Coastal Plain, eastern USA
Study regionThe study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA.Study focusGroundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex,
Authors
Leslie A. DeSimone, Jason P. Pope, Katherine Marie Ransom
A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA
Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m
Authors
Katherine M. Ransom, Bernard T. Nolan, Jonathan A. Traum, Claudia C. Faunt, Andrew M. Bell, Jo Ann M. Gronberg, David C. Wheeler, Celia Zamora, Bryant C. Jurgens, Gregory E. Schwarz, Kenneth Belitz, Sandra M. Eberts, George Kourakos, Thomas Harter
Non-USGS Publications**
Andy Canion, Katherine M. Ransom, Brian G. Katz; Discrimination of Nitrogen Sources in Karst Spring Contributing Areas Using a Bayesian Isotope Mixing Model and Wastewater Tracers (Florida, USA). Environmental and Engineering Geoscience ; 26 (3): 291–311. doi: https://doi.org/10.2113/EEG-2310
Ransom, K. M., Bell, A. M., Barber, Q. E., Kourakos, G., and Harter, T.: A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California, Hydrol. Earth Syst. Sci., 22, 2739–2758, https://doi.org/10.5194/hess-22-2739-2018, 2018
Willmes M, Ransom KM, Lewis LS, Denney CT, Glessner JJG, Hobbs JA (2018) IsoFishR: An application for reproducible data reduction and analysis of strontium isotope ratios (87Sr/86Sr) obtained via laser-ablation MC-ICP-MS. PLoS ONE 13(9): e0204519. https://doi.org/10.1371/journal.pone.0204519
Ransom, K. M., et al. (2016), Bayesian nitrate source apportionment to individual groundwater wells in the Central Valley by use of elemental and isotopic tracers, Water Resour. Res., 52, 5577– 5597, doi:10.1002/2015WR018523.
Li, X., Atwill, E.R., Antaki, E., Applegate, O., Bergamaschi, B., Bond, R.F., Chase, J., Ransom, K.M., Samuels, W., Watanabe, N. and Harter, T. (2015), Fecal Indicator and Pathogenic Bacteria and Their Antibiotic Resistance in Alluvial Groundwater of an Irrigated Agricultural Region with Dairies. J. Environ. Qual., 44: 1435-1447. https://doi.org/10.2134/jeq2015.03.0139
K.M. Lockhart, A.M. King, T. Harter,
Identifying sources of groundwater nitrate contamination in a large alluvial groundwater basin with highly diversified intensive agricultural production, Journal of Contaminant Hydrology, Volume 151, 2013, Pages 140-154, ISSN 0169-7722, https://doi.org/10.1016/j.jconhyd.2013.05.008.
Identifying sources of groundwater nitrate contamination in a large alluvial groundwater basin with highly diversified intensive agricultural production, Journal of Contaminant Hydrology, Volume 151, 2013, Pages 140-154, ISSN 0169-7722, https://doi.org/10.1016/j.jconhyd.2013.05.008.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.