.png){kind=icon}
返回
Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s11269-023-03677-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Dam outflow prediction
Long short-term memory
Input predictor selection
Hyperparameter optimization
期刊
IF:
4.7
论文数:
8.1K
被引数:
1.6W
机构
引用论文
分享收藏A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction.png)
JOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏Daily water level forecasting using wavelet decomposition and artificial intelligence techniquesJOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏Taking the Human Out of the Loop: A Review of Bayesian Optimization将人类带出循环: 贝叶斯优化的回顾PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-ValidationSUSTAINABILITY
OAAI
SUSTAINABILITYIF3.3
OAAI分享收藏
查看更多内容