Improving streamflow prediction in the WRF-Hydro model with LSTM networks-Reference-Cited by-同舟云学术

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Improving streamflow prediction in the WRF-Hydro model with LSTM networks

Published:2022-02 Issue: Volume:605 Page:127297
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Cho Kyeungwoo,Kim Yeonjoo

Funder

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Yonsei University

Korea Agency for Infrastructure Technology Advancement

Ministry of Land, Infrastructure and Transport

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference63 articles.

1. Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania;Appelhans;Spatial Statistics,2015

2. Role of runoff-infiltration partitioning and resolved overland flow on land-atmosphere feedbacks: a case study with the WRF-Hydro coupled modeling system for West Africa;Arnault;J. Hydrometeorol.,2016

3. Multi-time scale streamflow predictions: the support vector machines approach;Asefa;J. Hydrol.,2006

4. Random search for hyper-parameter optimization;Bergstra;J. Mach. Learn. Res.,2012

5. Toward improved calibration of hydrologic models: combining the strengths of manual and automatic methods;Boyle;Water Resour. Res.,2000

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