Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm
Author:
Funder
Ministry of Science and Technology
Publisher
Springer Science and Business Media LLC
Subject
Water Science and Technology,Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11269-023-03677-9.pdf
Reference62 articles.
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2. Ahmad SK, Hossain F (2019) A generic data-driven technique for forecasting of reservoir inflow: Application for hydropower maximization. Environ Model Softw 119:147–165. https://doi.org/10.1016/j.envsoft.2019.06.008
3. Aksoy H, Dahamsheh A (2018) Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions. J Hydrol 562:758–779. https://doi.org/10.1016/j.jhydrol.2018.05.030
4. Alizadeh B, Ghaderi Bafti A, Kamangir H, Zhang Y, Wright DB, Franz KJ (2021) A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction. J Hydrol 601:126526. https://doi.org/10.1016/j.jhydrol.2021.126526
5. Altman N, Krzywinski M (2015) Points of significance: Association, correlation and causation. Nat Methods 12(10)
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