Title :
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
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;
Conference_Titel :
Software Engineering, 2009. WCSE '09. WRI World Congress on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3570-8
DOI :
10.1109/WCSE.2009.131
