A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model
Author:
Funder
Fundamental Research Funds for the Central Universities
Natural Science Foundation of Hebei Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11269-024-03907-8.pdf
Reference33 articles.
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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. Frame JM, Kratzert F, Klotz D, Gauch M, Shalev G, Gilon O, Qualls LM, Gupta HV, Nearing GS (2022) Deep learning rainfall-runoff predictions of extreme events. Hydrol Earth Syst Sci 26(13):3377–3392
5. Gao S et al (2020) Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. J Hydrol 589. https://doi.org/10.1016/j.jhydrol.2020.125188
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