Abstract
Disastrous floods are destructive and likely to cause widespread economic losses. An understanding of flood forecasting and its potential forecast uncertainty is essential for water resource managers. Reliable forecasting may provide future streamflow information to assist in an assessment of the benefits of reservoirs and the risk of flood disasters. However, deterministic forecasting models are not able to provide forecast uncertainty information. To quantify the forecast uncertainty, a variational Bayesian neural network (VBNN) model for ensemble flood forecasting is proposed in this study. In VBNN, the posterior distribution is approximated by the variational distribution, which can avoid the heavy computational costs in the traditional Bayesian neural network. To transform the model parameters’ uncertainty into the model output uncertainty, a Monte Carlo sample is applied to give ensemble forecast results. The proposed method is verified by a flood forecasting case study on the upper Yangtze River. A point forecasting model neural network and two probabilistic forecasting models, including hidden Markov Model and Gaussian process regression, are also applied to compare with the proposed model. The experimental results show that the VBNN performs better than other comparable models in terms of both accuracy and reliability. Finally, the result of uncertainty estimation shows that the VBNN can effectively handle heteroscedastic flood streamflow data.
Highlights
Disastrous floods are destructive and likely to cause widespread economic losses [1]
In addition to the proposed variational Bayesian neural network (VBNN), a deterministic prediction model neural networks (NNs) and two probabilistic prediction models were developed for comparison, including Gaussian process regression (GPR) and hidden Markov model (HMM) [28]
VBNN: In this paper, the VBNN consisted of 1 input layer, 3 hidden layers, and 1 output layer
Summary
Disastrous floods are destructive and likely to cause widespread economic losses [1]. Since 1980, many kinds of forecast models have been developed for flood forecasting, such as the moving average model [4,5], the support vector machine [6,7], and neural networks (NNs) [8,9,10]. Most of these models only give a point forecast value without any forecast uncertainty information [11]. It is important to develop appropriate ensemble or probability forecasting models to provide forecast uncertainty information for water resources managers
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