A Hybrid Model for Real-Time Probabilistic Flood Forecasting Using Elman Neural Network with Heterogeneity of Error Distributions

Abstract

Traditional static neural networks often fail to describe dynamic flood processes, while recurrent neural networks can reflect this dynamic feature of flooding. In this paper, a real-time framework for probabilistic flood forecasting using an Elman neural network is presented. Based on this framework, flood forecasting models with different lead times are developed and trained by a real-time recurrent learning algorithm for forecasting the inflow of the Xianghongdian reservoir of the Huai River in East China. The performances of these models are evaluated. The forecasting model having a 3 h lead time meets the precision requirements and is chosen as the deterministic flood forecasting model. Compared with the multilayer perceptron having a 3 h lead time, the relative error of flood volume is 5.28% less, and the coefficient of efficiency is 0.105 greater. We further analyze the error characteristics of the selected model and derive the discharge probability density function based on the heterogeneity of error distributions. The forecasted discharge intervals with different confidence levels, the expected values, and the median values are obtained. The results show that the average relative errors of flood volume and peak discharge obtained by the median value forecasting are −1.66% and 5.69% respectively, and the coefficient of efficiency is 0.784. The performance of the median value forecasting was slightly better than that of the deterministic forecasting, and considerably better than that of the expected value forecasting. This study demonstrates that the proposed model has high practicability and can provide decision support for flood control.