Flow simulation based on MISO and LSTM models in the Yangtze River-Dongting Lake System
Year: 2025
Type: article
Abstract: Study region The study region is the Yangtze River-Dongting Lake system, the largest regulated river-lake system in China. Study focus Flow simulation of the Yangtze River–Dongting Lake system has long been recognized as a critical and challenging issue in the Yangtze River basin. Current approaches rely on traditional hydrological and hydrodynamic methods, but they face limitations such as difficulty obtaining required data set, complex modeling processes, long-time consumption, and insufficient accuracy. This study proposed the multi-input and single-output (MISO) model based on system dynamics principles and the long short-term memory (LSTM) neural network model based on deep learning for flow simulation in the Yangtze River-Dongting Lake system. New hydrological insights for the region The proposed MISO and LSTM models can achieve accurate flow simulation results using only recorded flow data and rainfall data in the Yangtze River-Dongting Lake system. Both models demonstrate significantly higher computational efficiency than traditional hydrodynamic models, the MISO model can complete simulation within one second, while the LSTM model has the higher Nash-Sutcliffe efficiency coefficients and the smaller relative errors during the training and validation periods, the peak discharge relative errors (PRE) is ranging from −1.40–1.81 % across twelve flood events. This study has provided a novel approach for flow simulation in the complex river-lake system.
Authors: Chenglong Li, Shenglian Guo, Zhen Cui, Xin Xiang
Institution: Wuhan University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,600
Awards: G4220179867, G8635708221
OpenAlex ID: W4410487364