Genetic algorithm based on wavelet neural network for the displacement prediction of landslide

Author

Ping Jiang ; Zhigang Zeng ; Jiejie Chen ; Tingwen Huang

Author_Institution

Sch. of Autom., Huazhong Univ. of Sci. & Technol., Wuhan, China

fYear

2014

fDate

28-29 Sept. 2014

Firstpage

1

Lastpage

5

Abstract

A method to predict the displacement of landside, genetic algorithm based on wavelet neural network (GAWNN) model, is presented in this paper. The hybrid model improves the predicting precision, which is compared with genetic algorithm based on back-propagation neural network (GABPNN). Furthermore, the hybrid model is applied for predicting the displacement of Baishuihe landslides in the Three Gorges reservoir area of China. The result shows the better accuracy than GABPNN in terms of the same measurements.

Keywords

backpropagation; genetic algorithms; geomorphology; geophysics computing; wavelet neural nets; Baishuihe landslide; GABPNN; GAWNN model; Three Gorges reservoir area; back-propagation neural network; displacement prediction; genetic algorithm; predicting precision; wavelet neural network; Genetic algorithms; Neural networks; Predictive models; Sociology; Statistics; Terrain factors; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014 International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6731-5

Type

conf

DOI

10.1109/MFI.2014.6997630

Filename

6997630