Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm

Vinh Ngoc Tran, Duc Dang Dinh, Binh Duy Huy Pham, Kha Dinh Dang, Tran Ngoc Anh, Ha Nguyen Ngoc and Giang Tien Nguyen ()
Additional contact information
Vinh Ngoc Tran: University of Michigan
Duc Dang Dinh: VNU University of Science, Vietnam National University, Thanh Xuan
Binh Duy Huy Pham: VNU University of Science, Vietnam National University, Thanh Xuan
Kha Dinh Dang: VNU University of Science, Vietnam National University, Thanh Xuan
Tran Ngoc Anh: VNU University of Science, Vietnam National University, Thanh Xuan
Ha Nguyen Ngoc: National Center for Water Resources Planning and Investigation (NAWAPI), Long Bien District
Giang Tien Nguyen: VNU University of Science, Vietnam National University, Thanh Xuan

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 2, No 1, 421 pages

Abstract: Abstract Reservoirs and dams are critical infrastructures that play essential roles in flood control, hydropower generation, water supply, and navigation. Accurate and reliable dam outflow prediction models are important for managing water resources effectively. In this study, we explore the application of three deep learning (DL) algorithms, i.e., gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), to predict outflows for the Buon Tua Srah and Hua Na reservoirs located in Vietnam. An advanced optimization framework, named the Bayesian optimization algorithm with a Gaussian process, is introduced to simultaneously select the input predictors and hyperparameters of DLs. A comprehensive investigation into the performance of three DLs in multistep-ahead prediction of outflow of two dams shows that all three models can predict the reservoir outflow accurately, especially for short lead-time predictions. The analysis results based on the root mean square error, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency indicate that BiLSTM and GRU are the most suitable models to diagnose the outflow of Buon Tua Srah and Hua Na reservoirs, respectively. Conversely, the results of the similarity assessment of 11 hydrological signatures show that LSTM outperforms BiLSTM and GRU in both case studies. This result emphasizes the importance of determining the purpose and objective function when choosing the best model for each case study. Ultimately, these results strengthen the potential of DL for efficient and effective reservoir outflow predictions to help policymakers and operators manage their water resource system operations better.

Keywords: Dam outflow prediction; Long short-term memory; Input predictor selection; Hyperparameter optimization (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-023-03677-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:38:y:2024:i:2:d:10.1007_s11269-023-03677-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-023-03677-9

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().