Prediction and Inference of Flow Duration Curves Using Multioutput Neural Networks-Reference-Cited by-同舟云学术

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Prediction and Inference of Flow Duration Curves Using Multioutput Neural Networks

Published:2019-08 Issue:8 Volume:55 Page:6850-6868
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Worland Scott. C.¹²^ORCID,Steinschneider Scott²^ORCID,Asquith William³^ORCID,Knight Rodney¹^ORCID,Wieczorek Michael⁴

Affiliation:

1. Lower Mississippi‐Gulf Water Science Center U.S. Geological Survey Nashville TN USA

2. Department of Biological and Environmental Engineering Cornell University Ithaca NY USA

3. Texas Water Science Center U.S. Geological Survey Lubbock TX USA

4. MD‐DE‐DC Water Science Center U.S. Geological Survey Catonsville MD USA

Funder

Gulf Coast Ecosystem Restoration Council

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR024463

Reference85 articles.

1. Archfield S. Vogel R. Steeves P. Brandt S. Weiskel P. &Garabedian S.(2009).The Massachusetts sustainable‐yield estimator: A decision‐support tool to assess water availability at ungaged sites in Massachusetts(2009‐5227): US Geological Survey Scientific Investigations Report. plus CD–ROM.

2. Entropy-based neural networks model for flow duration curves at ungauged sites

3. Prediction of flow duration curves for ungauged basins

4. Bingham R. H.(1979).Low‐flow characteristics of Alabama streams 117. Geological Survey of Alabama Division of Water Resources.

5. On the probability distribution of daily streamflow in the United States

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