An accurate rainfall–runoff observation is critical for giving a warning of a potential damage early enough to allow appropriate response to the disaster. The long short-term memory (LSTM)-based rainfall–runoff model has been proven to be effective in runoff prediction. Previous research has typically utilized multiple information sources as the LSTM training data. However, when there are many sequences of input data, the LSTM cannot get nonlinear valid information between consecutive data. In this paper, a novel informer neural network using empirical wavelet transform (EWT) was first proposed to predict the runoff based only on the single rainfall data. The use of EWT reduced the non-linearity and non-stationarity of runoff data, which increased the accuracy of prediction results. In addition, the model introduced the Fractal theory to divide the rainfall and runoff into three parts, by which the interference caused by excessive data fluctuations could be eliminated. Using 15-year precipitation from the GPM satellite and runoff from the USGS, the model performance was tested. The results show that the EWT_Informer model outperforms the LSTM-based models for runoff prediction. The PCC and training time in EWT_Informer were 0.937, 0.868, and 1 min 3.56 s, respectively, while those provided by the LSTM-based model were 0.854, 0.731, and 4 min 25.9 s, respectively.
The informer network was innovatively introduced into rainfall–runoff prediction, which reduced time and spatial complexity.
The empirical wavelet transform was utilized to enhance the treatment of non-linearity and non-stationarity.
Fractal theory was applied to eliminate the interference caused by excessive data fluctuations.