Comparison of multi-DLM approaches for predicting daily runoff: evidence from the data-driven model in one of China’s largest wheat production-bases-Reference-Cited by-同舟云学术

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Comparison of multi-DLM approaches for predicting daily runoff: evidence from the data-driven model in one of China’s largest wheat production-bases

Published:2023-07-31 Issue:41 Volume:30 Page:93862-93876
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Jia Shunqing,Wang Xihua^ORCID,Liu Zejun,Mao Boyang

Funder

Fundamental Research Funds for the Central Universities

Natural Science Foundation of Hebei Province

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-023-29030-6.pdf

Reference43 articles.

1. Apaydin H, Hajar F, Mohammad TS (2020) Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting. Water 12(5). https://doi.org/10.3390/w12051500

2. Bi X, Li B, Lu W, Zhou X (2020) Daily runoff forecasting based on data-augmented neural network model. J Hydroinf 22(4):900–915. https://doi.org/10.2166/hydro.2020.017

3. Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent Neural Networks for Multivariate Time Series with Missing Values. Sci Rep 8. https://doi.org/10.1038/s41598-018-24271-9

4. Chen H, Wang W, Chau K (2021) Flood Control Operation of Reservoir Group Using Yin-Yang Firefly Algorithm. Water Resour Manag 35(15):5325–5345. https://doi.org/10.1007/s11269-021-03005-z

5. Chen F, Su Y, Wang Y (2016) A novel prediction approach for runoff based on hybrid HMM-SVM Model, 5th International Conference on Sustainable Energy and Environment Engineering (ICSEEE). AER-Advances in Engineering Research, Shenzhen, PEOPLES R CHINA,63:135-139

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1. A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model;Water Resources Management;2024-06-10

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