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.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Carrera, J.: Estimation Of Aquifer Parameters Under Transient And Steady State Conditions. Water Resources Research 22, 199–210 (1986)
Yeh, W.: Review Of Parameter Identification Procedures In Groundwater Hydrology: The Inverse Problem. Water Resources Research 22, 95–108 (1986)
Huang, Y.: Application Of Artificial Neural Networks To Predictions of Aggregate Quality Parameters. Int. J. of Rock Mechanics and Mining Sciences 36, 551–561 (1999)
Najjar, Y.M.: Utilizing Computational Neural Networks For Evaluating the Permeability of Compacted Clay Liners. Geotechnical and Geological Engineering 14, 193–212 (1996)
Cao, X.: Application Of Artificial Neural Networks To Load Identification. Computers & Structures 69, 63–78 (1998)
Huang, C.S.: A Neural Network Approach For Structural Identification And Diagnosis Of A Building From Seismic Response Data. Earthquake Engineering and Structural Dynamics 32, 187–206 (2003)
Lightbody, G.: Multi-Layer Perceptron Based Modeling of Nonlinear Systems. Fuzzy Sets and System 79, 93–112 (1996)
Oh, S.K.: Parameter Estimation of Fuzzy Controller and its Application to Inverted Pendulum. Engineering Applications of Artificial Intelligence 17, 37–60 (2004)
Paccella, M.: Manufacturing Quality Control by Means of a Fuzzy ART Network Trained on Natural Process Data. Engineering Applications of Artificial Intelligence 17, 83–96 (2004)
Denton, J.W.: A Comparison of Nonlinear Optimization Methods for Supervised Learning in Multilayer Feedforward Neural Networks. European Journal of Operational Research 93, 358–368 (1996)
Meulenkamp, F.: Application of Neural Networks for the Prediction of the Unconfined Compressive Strength from Equotip Hardness. Int. J. of Rock Mechanics and Mining Sciences 36, 29–39 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)