A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists-Reference-Cited by-同舟云学术

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A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Published:2018-11 Issue:11 Volume:54 Page:8558-8593
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Shen Chaopeng¹^ORCID

Affiliation:

1. Civil and Environmental Engineering Pennsylvania State University University Park PA 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/10.1029/2018WR022643

Reference274 articles.

1. Neural Error Regression Diagnosis (NERD): A Tool for Model Bias Identification and Prognostic Data Assimilation

2. Systematic Bias in Land Surface Models

3. Doing ecohydrology backward: Inferring wetland flow and hydroperiod from landscape patterns

4. Estimating soil moisture using remote sensing data: A machine learning approach

5. A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations

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