Multi-phase hybrid bidirectional deep learning model integrated with Markov chain Monte Carlo bivariate copulas function for streamflow prediction
Author:
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/s00477-023-02632-9.pdf
Reference100 articles.
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2. Adnan RM, Petroselli A, Heddam S, Santos CAG, Kisi O (2021) Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Nat Hazards 105:2987–3011. https://doi.org/10.1007/s11069-020-04438-2
3. Ahmed AM, Ahmed MH, Saha SK, Ahmed O, Sutradhar A (2022) Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model. Stoch Env Res Risk Assess 36(10):3011–3039. https://doi.org/10.1007/s00477-022-02177-3
4. Ali S, Shahbaz M (2020) Streamflow forecasting by modeling the rainfall–streamflow relationship using artificial neural networks. Model Earth Syst Environ 6:1645–1656. https://doi.org/10.1007/s40808-020-00780-3
5. Alomari ES, Nuiaa RR, Alyasseri ZAA, Mohammed HJ, Sani NS, Esa MI, Musawi BA (2023) Malware detection using deep learning and correlation-based feature selection. Symmetry 15(1):123. https://doi.org/10.3390/sym15010123
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