Skip to main content

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

Dates

Publication Date

Citation

Katherine M. Ransom, Bernard T. Nolan, Jonathan A. Traum, Claudia Faunt, Andrew M. Bell, Jo Ann M. Gronberg, David C. Wheeler, Celia Zamora, Bryant Jurgens, Gregory E. Schwarz, Kenneth Belitz, Sandra Eberts, George Kourakos, and Thomas Harter, 2017, A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA: Science of the Total Environment, v. 601-602.

Summary

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 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 [...]

Contacts

Attached Files

Thumbnail
Thumbnail

Tags

Additional Information

Identifiers

Type Scheme Key
local-index unknown 70187880
local-pk unknown 70187880
doi http://www.loc.gov/standards/mods/mods-outline-3-5.html#identifier doi:10.1016/j.scitotenv.2017.05.192
series unknown Science of the Total Environment

Citation Extension

citationTypeArticle
journalScience of the Total Environment
languageEnglish
parts
typevolume
value601-602

Item Actions

View Item as ...

Save Item as ...

View Item...