A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation

Abstract

Precise long-term runoff prediction holds crucial significance in water resource management. Although the long short-term memory (LSTM) model is widely adopted for long-term runoff prediction, it encounters challenges such as error accumulation and low computational efficiency. To address these challenges, we utilized a novel method to predict runoff based on a Transformer and the base flow separation approach (BS-Former) in the Ningxia section of the Yellow River Basin. To evaluate the effectiveness of the Transformer model and its responsiveness to the base flow separation technique, we constructed LSTM and artificial neural network (ANN) models as benchmarks for comparison. The results show that Transformer outperforms the other models in terms of predictive performance and that base flow separation significantly improves the performance of the Transformer model. Specifically, the performance of BS-Former in predicting runoff 7 days in advance is comparable to that of the BS-LSTM and BS-ANN models with lead times of 4 and 2 days, respectively. In general, the BS-Former model is a promising tool for long-term runoff prediction. HIGHLIGHTS The effectiveness of a Transformer model for simulating and predicting long-term daily runoff is explored.; The response of the Transformer model to prior hydrological knowledge in base flow separation is analyzed.; The potential of base flow separation techniques to improve the ability to predict the runoff lead time in deep learning models is explored.;

Keywords

• attention mechanism
• coupled model
• daily runoff prediction
• lstm
• transformer