Guidance on the construction and selection of relatively simple to complex data-driven models for multi-task streamflow forecasting
Author:
Funder
2024 Research Fund of University of Ulsan
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00477-024-02776-2.pdf
Reference66 articles.
1. 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
2. Ahn S, Tran TD, Kim J (2022) Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast. Ocean Eng 264. https://doi.org/10.1016/j.oceaneng.2022.112593
3. 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. https://doi.org/10.1016/j.jhydrol.2021.126526
4. Ashrafzadeh A, Salehpoor J, Lotfirad M (2024) Comparative analysis of data-driven and conceptual streamflow forecasting models with uncertainty assessment in a major basin in Iran. Int J Energy Water Resour. https://doi.org/10.1007/s42108-023-00276-7
5. Aufa BZ, Suyanto S, Arifianto A (2020) Hyperparameter setting of LSTM-based language model using grey wolf optimizer, paper presented at 2020 international conference on data science and its applications (ICoDSA), IEEE
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