Details

Autor(en) / Beteiligte

• Xu, Yuanhao
• Hu, Caihong
• Wu, Qiang
• Jian, Shengqi
• Li, Zhichao
• Chen, Youqian
• Zhang, Guodong
• Zhang, Zhaoxi
• Wang, Shuli

Titel

Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation

Ist Teil von

• Journal of hydrology (Amsterdam), 2022-05, Vol.608, p.127553, Article 127553

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Provides a protocol to optimize hyperparameters of LSTM.•Proposes a PSO-LSTM model for flood simulation and forecast.•Study the effect of hyperparameter changes on LSTM results.•Explores the optimal hyperparameter combination of LSTM at different lead times in flood forecast, and achieve the best simulation results by optimizing model hyperparameters.•Compares the performance of the models including physical and neural network models. Flood forecasting is an essential non-engineering measure for flood prevention and disaster reduction. Many models have been developed to study the complex and highly random rainfall-runoff process. In recent years, artificial intelligence methods, such as the artificial neural network (ANN), have attempted to construct rainfall-runoff models. The more advanced deep learning methods of long short-term memory (LSTM) network have been proved to better predict hydrological time series. However, the selection of LSTM hyperparameters in the past mostly relied on the experience of the staff, which often led to failure to achieve the best performance. The aim of this study is to develop a method to improve flood forecast accuracy and lead time. A deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed in this paper. The PSO algorithm was used to optimize the LSTM hyperparameter to improve the ability to learn data sequence features. The model focuses on the Jingle Watershed in the Fenhe River and the Lushi Watershed in the Luohe River and was used to predict flood processes using rainfall and runoff observation data from stations in the watersheds. We evaluated the performance of the model with the Nash Sutcliffe efficiency coefficient, root mean square error, and bias. The results show that the PSO-LSTM model outperforms the M-EIES, ANN, PSO-ANN, and LSTM at all stations in the watersheds. The PSO-LSTM model improves the flood forecasting accuracy at different lead times, especially for those exceeding 6 h, and has higher prediction accuracy and stability. The PSO-LSTM model could be used to improve accuracy in short-term flood forecast applications.