A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs-Reference-Cited by-同舟云学术

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A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs

Published:2024-02 Issue: Volume:629 Page:130608
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Ngoc Tran Vinh,Ivanov Valeriy Y.,Tien Nguyen Giang,Ngoc Anh Tran,Huy Nguyen Phuong,Kim Dae-Hong,Kim Jongho

Funder

Korea Environmental Industry and Technology Institute

National Science Foundation

National Research Foundation of Korea

Ministry of Science and Technology

Vietnam National University

University of Michigan

University of Ulsan

Ministry of Environment

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Reference125 articles.

1. The quest for model uncertainty quantification: A hybrid ensemble and variational data assimilation framework;Abbaszadeh;Water Resour. Res.,2019

2. Abbot, J., and J. Marohasy (2014), Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks, Atmospheric Research, 138, 166-178.

3. A review of uncertainty quantification in deep learning: Techniques, applications and challenges;Abdar;Information Fusion,2021

4. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds;Adamowski;J. Hydrol.,2010

5. A generic data-driven technique for forecasting of reservoir inflow: Application for hydropower maximization;Ahmad;Environ. Model. Softw.,2019

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