Bayesian probabilistic forecasting model based on BP ANN
Jianyi Lin · Journal of Hydraulic Engineering · 2006
Based on the Bayesian Forecasting System(BFS) framework,a new prior density and likelihood function model using BP artificial neural network(ANN) is developed to study the hydrologic uncertainty of the Shuangpai Reservoir,China.The Markov chain Monte Carlo method is applied to solve the posterior distribution and statistics of reservoir stage.The study result of the floods in history shows that Bayesian probabilistic forecasting model based on BP ANN not only remarkably improves the forecasting precision but also offers more information for flood control,which makes it possible for decision makers to consider the uncertainty of hydrologic forecasting during decision-making and estimate the risks of different decisions quantitatively.