Hydrologie uncertainty for bayesian probabilistic forecasting model based on BP ANN

Chun Tian Cheng, Kwok Wing Chau, Xiang Yang Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)


The Bayesian forecasting system (BFS) consists of three components which can be deal with independently. Considering the fact that the quantitative rainfall forecasting has not been fully developed in all catchment areas in China, the emphasis is given to the hydrologie uncertainty for Bayesian probabilistic forecasting. The procedure of determining the prior density and likelihood functions associated with hydrologie uncertainty is very complicated and there is a requirement to assume a linear and normal distribution within the framework of BFS. These pose severe limitation to its practical application to real-life situations. In this paper, a new prior density and likelihood function model is developed with BP artificial neural network (ANN) to study the hydrologic uncertainty of short-term reservoir stage forecasts based on the BFS framework. Markov chain Monte Carlo (MCMC) method is employed to solve the posterior distribution and statistics of reservoir stage. A case study is presented to investigate and illustrate these approaches using 3 hours rainfall-runoff data from the ShuangPai Reservoir in China. The results show that Bayesian probabilistic forecasting model based on BP ANN not only increases forecasting precision greatly but also offers more information for flood control, which makes it possible for decision makers consider the uncertainty of hydrologie forecasting during decision-making and estimate risks of different decisions quantitatively.
Original languageEnglish
Title of host publicationProceedings - Third International Conference on Natural Computation, ICNC 2007
Number of pages6
Publication statusPublished - 1 Dec 2007
Event3rd International Conference on Natural Computation, ICNC 2007 - Haikou, Hainan, China
Duration: 24 Aug 200727 Aug 2007


Conference3rd International Conference on Natural Computation, ICNC 2007
CityHaikou, Hainan

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics
  • Modelling and Simulation

Cite this