Wavelet neural network based on BP algorithm and its application in flood forecasting

Author

Hu Ping

Author_Institution

Coll. of Sci., Wuhan Univ. of Sci. & Eng., Wuhan, China

fYear

2009

fDate

17-19 Aug. 2009

Firstpage

251

Lastpage

253

Abstract

As is well known, it is the application of runoff flood forecasting that is extraordinary significant for us. A detailed detection of the flood forecasting process has been carried out using powerful artificial neural network in this paper. Learning algorithm of wavelet neural network was produced by extruding it in BP idea.The determination of network hidden layer nodes utilizes the method of tring fault. Activation function belongs to morlet wavelet fund ion,and the module of net structure belongs to 371. It is shown that the reliable prediction accury could be provided by using this model for predicting and analysing for the flood data of solar Da in 1996.

Keywords

floods; geophysics computing; learning (artificial intelligence); neural nets; wavelet transforms; BP algorithm; activation function; artificial neural network; flood forecasting; learning algorithm; network hiddenlayer nodes; string fault; wavelet neural network; Artificial neural networks; Educational institutions; Fault tolerance; Feedforward neural networks; Floods; Management training; Neural networks; Power engineering and energy; Predictive models; Wavelet analysis; BP algorithm; flood; forecasting; neural network; wavelet;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing, 2009, GRC '09. IEEE International Conference on

Conference_Location

Nanchang

Print_ISBN

978-1-4244-4830-2

Type

conf

DOI

10.1109/GRC.2009.5255121

Filename

5255121