Flood forecasting and uncertainty analysis based on the combination of improved adaptive noise learning model and density estimation

Abstract

The strong randomness exhibited by the runoff series makes the accuracy of the flood forecasting still needs to be improved. Mode mixing can be dealt with using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the endpoint effect of CEEMDAN can be successfully dealt with using the mutual information criterion. To increase the computational effectiveness of broad learning (BL), orthogonal triangular matrix decomposition (QR) was used. A novel improved coupled CEEMDAN-QRBL flood forecasting model was created and applied to the prediction of daily runoff in Xiaolangdi reservoir based on the benefit of quick calculation of the model output layer. The findings indicate that the enhanced QRBL is 28.92% more computationally efficient than the BL model, and that the reconstruction error of CEEMDAN has been decreased by 48.22%. The MAE of the improved CEEMDAN-QRBL model is reduced by 12.36% and 16.31%, and the Ens is improved by 8.81% and 3.96%, respectively, when compared to the EMD-LSTM and CEEMDAN-GRU model. The predicted values of CEEMDAN-QRBL model have a suitable fluctuation range thanks to the use of nonparametric kernel density estimation (NPKDE), which might serve as a useful benchmark for the distribution of the regional water resources.