Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.-Reference-Cited by-同舟云学术

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Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.

Published:2017-05 Issue:5 Volume:53 Page:3878-3895
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Sahoo S.¹^ORCID,Russo T. A.¹^ORCID,Elliott J.²³^ORCID,Foster I.²³⁴^ORCID

Affiliation:

1. Department of GeosciencesPennsylvania State UniversityUniversity Park Pennsylvania USA

2. Computation Institute, University of ChicagoChicago Illinois USA

3. Mathematics and Computer Science Division, Argonne National LaboratoryLemontIllinois USA

4. Department of Computer ScienceUniversity of ChicagoChicago Illinois USA

Funder

National Science Foundation

U.S. Department of Energy

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/2016WR019933

Reference93 articles.

1. A wavelet neural network conjunction model for groundwater level forecasting

2. Dry matter and qualitative characteristics of alfalfa as affected by harvest times and soil water content

3. Flow and Storage in Groundwater Systems

4. Artificial Neural Networks in Hydrology. I: Preliminary Concepts

5. A Technique for Maximizing Details in Numerical Weather Map Analysis

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