Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin

The long short-term memory network (LSTM) model alleviates the gradient vanishing or exploding problem of the recurrent neural network (RNN) model with gated unit architecture. It has been applied to flood forecasting work. However, flood data have the characteristic of unidirectional sequence transmission, and the gated unit architecture of the LSTM model establishes connections across different time steps which may not capture the physical mechanisms or be easily interpreted for this kind of data. Therefore, this paper investigates whether the gated unit architecture has a positive impact and whether LSTM is still better than RNN in flood forecasting work. We establish LSTM and RNN models, analyze the structural differences and impacts of the two models in transmitting flood data, and compare their performance in flood forecasting work. We also apply hyperparameter optimization and attention mechanism coupling techniques to improve the models, and establish an RNN model for optimizing hyperparameters using BOA (BOA-RNN), an LSTM model for optimizing hyperparameters using BOA (BOA-LSTM), an RNN model with MHAM in the hidden layer (MHAM-RNN), and an LSTM model with MHAM in the hidden layer (MHAM-LSTM) using the Bayesian optimization algorithm (BOA) and the multi-head attention mechanism (MHAM), respectively, to further examine the effects of RNN and LSTM as the underlying models and of cross-time scale bridging for flood forecasting. We use the measured flood process data of LouDe and HuaYuankou stations in the Yellow River basin to evaluate the models. The results show that compared with the LSTM model, under the 1 h forecast period of the LouDe station, the RNN model with the same structure and hyperparameters improves the four performance indicators of the Nash-Sutcliffe efficiency coefficient (NSE), the Kling-Gupta efficiency coefficient (KGE), the mean absolute error (MAE), and the root mean square error (RMSE) by 1.72%, 4.43%, 35.52% and 25.34%, respectively, and the model performance of the HuaYuankou station also improves significantly. In addition, under different situations, the RNN model outperforms the LSTM model in most cases. The experimental results suggest that the simple internal structure of the RNN model is more suitable for flood forecasting work, while the cross-time bridging methods such as gated unit architecture may not match well with the flood propagation process and may have a negative impact on the flood forecasting accuracy. Overall, the paper analyzes the impact of model architecture on flood forecasting from multiple perspectives and provides a reference for subsequent flood forecasting modeling.