Please use this identifier to cite or link to this item:
|Title:||Hydrologic uncertainty for Bayesian probabilistic forecasting model based on BP ANN|
Monte Carlo methods
|Publisher:||IEEE Computer Society|
|Source:||ICNC 2007 : proceedings of the Third International Conference on Natural Computation, Haikou, Hainan, China, 24-27 Aug, 2007, v. 1, p. 197-201 How to cite?|
|Abstract:||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 hydrologic uncertainty for Bayesian probabilistic forecasting. The procedure of determining the prior density and likelihood functions associated with hydrologic 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 hydrologic forecasting during decisionmaking and estimate risks of different decisions quantitatively.|
|Rights:||© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
|Appears in Collections:||Conference Paper|
Show full item record
Citations as of Apr 30, 2016
WEB OF SCIENCETM
Citations as of Jan 24, 2017
Checked on Feb 19, 2017
Checked on Feb 19, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.