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A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

Overview of attention for article published in Science of the Total Environment, December 2017
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1 tweeter

Citations

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62 Dimensions

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175 Mendeley
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Title
A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA
Published in
Science of the Total Environment, December 2017
DOI 10.1016/j.scitotenv.2017.05.192
Pubmed ID
Authors

Katherine M. Ransom, Bernard T. Nolan, Jonathan A. Traum, Claudia C. Faunt, Andrew M. Bell, Jo Ann M. Gronberg, David C. Wheeler, Celia Z. Rosecrans, Bryant Jurgens, Gregory E. Schwarz, Kenneth Belitz, Sandra M. Eberts, George Kourakos, Thomas Harter

Abstract

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 500m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50ppb and probability of dissolved oxygen concentration to be below 0.5ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971-2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 175 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 25%
Researcher 32 18%
Student > Master 28 16%
Student > Doctoral Student 10 6%
Student > Postgraduate 7 4%
Other 20 11%
Unknown 35 20%
Readers by discipline Count As %
Earth and Planetary Sciences 32 18%
Environmental Science 31 18%
Engineering 17 10%
Agricultural and Biological Sciences 16 9%
Computer Science 9 5%
Other 24 14%
Unknown 46 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 June 2017.
All research outputs
#9,902,369
of 15,557,767 outputs
Outputs from Science of the Total Environment
#9,145
of 14,355 outputs
Outputs of similar age
#156,936
of 269,039 outputs
Outputs of similar age from Science of the Total Environment
#70
of 94 outputs
Altmetric has tracked 15,557,767 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,355 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,039 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.