Japan Geoscience Union Meeting 2023

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-TT Technology &Techniques

[A-TT29] Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions

Mon. May 22, 2023 1:45 PM - 3:00 PM Exhibition Hall Special Setting (4) (Exhibition Hall 8, Makuhari Messe)

convener:Venkata Ratnam Jayanthi(Application Laboratory, JAMSTEC), Patrick Martineau(Japan Agency for Marine-Earth Science and Technology), Takeshi Doi(JAMSTEC), Swadhin Behera(Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001), Chairperson:Patrick Martineau(Japan Agency for Marine-Earth Science and Technology), Takeshi Doi(JAMSTEC), Venkata Ratnam Jayanthi(Application Laboratory, JAMSTEC)

2:30 PM - 2:45 PM

[ATT29-04] Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation

*Caihong Hu1, Chengshuai Liu1, Tianning Xie1, Wenzhong Li1, Yuanhao Xu1 (1.Yellow River Laboratory of Zhengzhou University)

Keywords:Long short-term memory, Particle swarm optimization, Rainfall-runoff, Flood forecasting, Deep learning

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.