Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Link
https://link.springer.com/content/pdf/10.1007/s11069-023-05877-3.pdf
Reference51 articles.
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2. Adaryani FR, Jamshid Mousavi S, Jafari F (2022) Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN. J Hydrol 614:128463. https://doi.org/10.1016/j.jhydrol.2022.128463
3. Asaad MN, Eryürük Ş, Eryürük K (2022) Forecasting of streamflow and comparison of artificial intelligence methods: a case study for Meram stream in Konya Turkey. Sustainability 14:6319. https://doi.org/10.3390/su14106319
4. Barbaros F, Onuşluel Gül G, Boyacioğlu H (2021) Evaluation of seasonality in water quality with non-parametric statistical methods in the sample of Küçük Menderes Basin. J Suleyman Demirel Univ Insti Sci Nat Appl Sci 25(2):195–207. https://doi.org/10.19113/sdufenbed.790331
5. Cao Q, Banerjee R, Gupta S, et al (2016) Data driven production forecasting using machine learning. In: Day 2 Thu, June 02, 2016. SPE, Buenos Aires, pp D021S006R001
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