Abstract
In the past, typical approaches simulating the groundwater flow are based on continuous trials to approach to the targeted measurement accuracy. In this study, we propose a new neural network based on feedback observer technique to estimate the hydro-geological structure and hydraulic parameters of a large-scale alluvial fan in Taiwan. We develop an under-ground water level observer (UGW-LO) based on feedback control theory to simulate the dynamics of groundwater levels and estimate water levels of wells in the large area. In the proposed observer system, a large-scale back-propagation neural network (BPNN) is proposed to simulate water levels dynamics of multiple wells. The simulation results are fed back as a reference for BPNN to approach to refine estimation. Based on that model, a groundwater flow is simulated correctly by software MODFLOW. Experimental results indicate that the innovative method works better than conventional regression estimations. The learning ability of BPNN also contributes to overcome the gap between legacy dynamics UGW equations and real UGW dynamics. The applicability and precision are verified in a large scale experiment that is beneficial to the management of under-ground waters and reduce the risk of ground–sink.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ildoromi, A.: Analysis and Modeling of Landslide Surface with Geometrical Hydrology and Slope Stability: Case Study Ekbatan Watershed Basin, Hamedan, Iran 6(3), 920–924 (2012)
Rimmer, A., Hartmann, A.: Simplified Conceptual Structures and Analytical Solutions for Groundwater Discharge Using Reservoir Equations. In: Water Resources Management and Modeling. InTech (2012) ISBN 978-953-51-0246-5
Wang, H., Anderson, M.P.: Introduction to groundwater modeling finite differences and finite element methods. Elsevier (1995) ISBN-10: 012734585X
Chang, F.C., Huang, H.L., Chang, L.T.: Application of recurrent neural networks on flow estimation of rivers. Journal of Argi. Eng. 49(2), 32–39 (2001)
Liu, C.Y., Kuo, Y.M.: Variation analysis of underground water in Yulin area based on back-propagation neural networks. Journal of Taiwan Water Conservancy 48(1), 9–25 (2000)
Dawson, C.W., Wilby, R.L.: Hydrological modeling using artificial neural networks. Progress in Physical Geography 25(1), 80–108 (2001)
Lin, S.W., Chou, S.Y., Chen, S.C.: Irregular shapes classification by back-propagation neural networks. Journal of Advance Manufacturing Technology 34, 1164–1172 (2007)
Chang, F.C., Hsu, R.T.: Application of grey theory on real-time dam operation. Journal of Taiwan Water Conservancy 47(1), 44–53 (1999)
Shiao, J.T., Chang, L.C.: Application of dynamic control and genetic algorithms on the management and operation of underground water resource. In: Proceeding of the Tenth Conference on Water Conservancy, pp. 40–45 (1990)
Ding, T.S., Cao, C.M., Chen, C.Y.: A study of pollution management on underground water. Journal of Technology 11(3), 159–169 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Hung, CY., Lee, CH. (2014). An Optimized Approach Based on Neural Network and Control Theory to Estimate Hydro-geological Parameters. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-07455-9_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07454-2
Online ISBN: 978-3-319-07455-9
eBook Packages: Computer ScienceComputer Science (R0)