Neural Network Analog on Dynamic Variation of the Karst Water and the Prediction for Spewing Tendency of Springs in Jinan

Author

Chen, Xuequn ; Li, Fulin ; Liu, Ye ; Yan, Chengshan ; Lin, Lin

Author_Institution

Water Conservancy Res. Inst. of Shandong Province, Jinan, China

Volume

3

fYear

2009

fDate

19-21 May 2009

Firstpage

321

Lastpage

324

Abstract

Considering the factors that affect the karst water level, the improved neural network model has been applied to construct the random model that analogs the dynamic change of karst water. The accuracy of our analog has been greatly improved compared with that of multi-line recurrence model; moreover, BP model has strong functions of study, fault tolerance and association. In a word, BP model is an effective tool to predict the dynamic change of karst water. In addition, the spewing tendency of springs in Jinan is analyzed based on our prediction results in this paper.

Keywords

backpropagation; environmental science computing; fault tolerance; groundwater; neural nets; water; water supply; BP model; Jinan; dynamic variation; fault tolerance; karst water; multiline recurrence model; neural network; spewing tendency prediction; springs; Artificial neural networks; Biological neural networks; Cities and towns; Neural networks; Neurons; Numerical models; Parameter estimation; Predictive models; Springs; Water conservation; BP Neutral Network; Karst water; Predict;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, 2009. WCSE '09. WRI World Congress on

Conference_Location

Xiamen

Print_ISBN

978-0-7695-3570-8

Type

conf

DOI

10.1109/WCSE.2009.131

Filename

5319418