Abstract
The mathematical model of underground water flow is introduced as basis to identify the permeability coefficients of rock foundation by observing the water heads of the underground water flow. The artificial neural network is applied to estimate the permeability coefficients. The weights of neural network are trained by using BFGS optimization algorithm and the Levenberg-Marquardt approximation which have a fast convergent ability. The parameter identification results illustrate that the proposed neural network has not only higher computing efficiency but also better identification accuracy. According to identified permeability coefficients of the rock foundation, the seepage field of gravity dam and its rock foundation is computed by using finite element method. The numerically computational results with finite element method show that the forecasted water heads at observing points according to identified parameters can precisely agree with the observed water heads.
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, S., Liu, Y. (2005). Parameter Identification Procedure in Groundwater Hydrology with Artificial Neural Network. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_29
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DOI: https://doi.org/10.1007/11538356_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28227-3
Online ISBN: 978-3-540-31907-8
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