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

Abstract

The strong randomness exhibited by runoff series means the accuracy of 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 by 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 with the EMD-LSTM and CEEMDAN-GRU models. The predicted values of the 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 regional water resources. HIGHLIGHTS A novel CEEMDAN-QRBL model for flood forecasting was constructed.; Orthogonal triangular matrix decomposition was used to improve broad learning to enhance its computational efficiency.; The mutual information criterion was used to improve CEEMDAN to suppress the endpoint effect of CEEMDAN.; Nonparametric kernel density estimation was used to analyse the confidence level of flood forecasting.;

Keywords

• broad learning
• endpoint effect
• flood forecasting
• nonparametric kernel density estimation
• orthogonal triangular matrix decomposition