Real-time flood forecasting based on a general dynamic neural network framework-Reference-Cited by-同舟云学术

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Real-time flood forecasting based on a general dynamic neural network framework

Published:2022-07-29 Issue:1 Volume:37 Page:133-151
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Wan Xinyu,Wu Qingyang,Cao Zhenyu,Wu Yan

Funder

The National Key Research and Development Program of China

The National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-022-02271-6.pdf

Reference49 articles.

1. Atabay D (2016) pyrenn: First release (Version v0.1). Zenoto. https://doi.org/10.5281/zenodo.45022

2. Anam S (2019) Rainfall prediction using backpropagation algorithm optimized by Broyden-Fletcher-Goldfarb-Shanno algorithm. IOP Conf Ser: Mater Sci Eng 567:012008. https://doi.org/10.1088/1757-899X/567/1/012008

3. Ateeq-ur-Rauf GAR, Ahmad S, Hashmi HN (2018) Performance assessment of artificial neural networks and support vector regression models for stream-flow predictions. Environ Monit Assess 190:704. https://doi.org/10.1007/s10661-018-7012-9

4. Chang F-J, Chang L-C, Huang H-L (2002) Real-time recurrent learning neural network for stream-flow forecasting. Hydrol Process 16:2577–2588. https://doi.org/10.1002/hyp.1015

5. Chang L-C, Chang F-J, Chiang Y-M (2004) A two-step-ahead recurrent neural network for stream-flow forecasting. Hydrol Process 18:81–92. https://doi.org/10.1002/hyp.1313

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4. MANAGEMENT OF FLOOD AND SEPARATING DAMAGE FROM ENVIRONMENTAL EFFECTS USING BAYESIAN CONVOLUTIONAL NEURAL NETWORK;Global NEST: the international Journal;2023-09-06

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