Abstract: Hydrological time series are affected by rainfall, showing seasonal and non-linear characteristics. The traditional single model is simple in structure, which has the problems of low prediction accuracy for complex nonlinear hydrological time series and can not capture the composite characteristics of hydrological time series well. The combined forecasting model adopts the idea of multi-classifier, which can effectively improve the forecasting accuracy. However, it is time-consuming and inaccurate to manually adjust the model parameters. In this paper, a combined forecasting model based on SFLA-CNN and LSTM is proposed: The parameters of CNN model are optimized by Shuffled Frog Leaping Algorithm, and the optimized SFLA-CNN forecasting model is obtained; After that, BP neural network is used to nonlinearly combine the predicted values of SFLA-CNN and LSTM models to obtain the final prediction results. The experimental results of water level prediction in Taihu Lake region of Jiangsu Province show that the combined model has effectively improved the prediction accuracy and better generalization ability compared with the existing models.

Key words: water level prediction, shuffled frog leaping algorithm, convolutional neural network, long and short-term memory network, combination model