Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning-Reference-Cited by-同舟云学术

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Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

Published:2019-12 Issue:12 Volume:55 Page:11344-11354
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Kratzert Frederik¹^ORCID,Klotz Daniel¹^ORCID,Herrnegger Mathew²,Sampson Alden K.³,Hochreiter Sepp¹,Nearing Grey S.⁴^ORCID

Affiliation:

1. LIT AI Lab and Institute for Machine Learning Johannes Kepler University Linz Austria

2. Institute for Hydrology and Water Management University of Natural Resources and Life Sciences Vienna Austria

3. Upstream Tech Natel Energy Inc. Alameda CA USA

4. Department of Geological Sciences University of Alabama Tuscaloosa AL USA

Funder

Robert Bosch (Australia) Pty

Austrian Science Fund

Google

Nvidia

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019WR026065

Reference46 articles.

1. A Ranking of Hydrological Signatures Based on Their Predictability in Space

2. Addor N. Newman A. Mizukami N. &Clark M. P.(2017).Catchment attributes for large‐sample studies.https://doi.org/10.5065/D6G73C3Q

3. The CAMELS data set: catchment attributes and meteorology for large-sample studies

4. End-to-End Differentiable Learning of Protein Structure

5. Predictions in ungauged basins – where do we stand?

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