A Hybrid of Artificial Neural Networks and Particle Swarm Optimization Algorithm for Inverse Modeling of Leakage in Earth Dams
A new intelligent hybrid method for inverse modeling (Parameter Identification) of leakage from the body and foundation of earth dams considering transient flow model has been presented in this paper. The main objective is to determine the permeability in different parts of the dams using observation data. An objective function which concurrently employs time series of hydraulic heads and flow rates observations has been defined to overcome the ill-posedness issue (nonuniqueness and instability of the identified parameters). A finite element model which considers all construction phases of an earth dam has been generated and then orthogonal design, back propagation artificial neural network and Particle Swarm Optimization algorithm has been used simultaneously to perform inverse modeling. The suggested method has been used for inverse modeling of seepage in Baft dam in Kerman, Iran as a case study. Permeability coefficients of different parts of the dam have been inspected for three distinct predefined cases and in all three cases excellent results have been attained. The highly fitting results confirm the applicability of the recommended procedure in the inverse modeling of real large-scale problems to find the origin of leakage channels which not only reduces the calculation cost but also raises the consistency and efficacy in such problems.
Ghamisi, Pedram, Micael S. Couceiro, Fernando M. L. Martins, and Jon Atli Benediktsson. “Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization.” IEEE Transactions on Geoscience and Remote Sensing 52, no. 5 (May 2014): 2382–2394. doi:10.1109/tgrs.2013.2260552.
Chang, Wei-Der, Jun-Ping Cheng, Ming-Chieh Hsu, and Liang-Chan Tsai. “Parameter Identification of Nonlinear Systems Using a Particle Swarm Optimization Approach.” 2012 Third International Conference on Networking and Computing (December 2012). doi:10.1109/icnc.2012.24.
Durmuş, B., and A. Gün. "Parameter identification using particle swarm optimization." In Proceedings, 6th International Advanced Technologies Symposium, (IATS 11), Elazığ, Turkey, (2011): 188-192.
Fernández Martínez, Juan L., Esperanza García Gonzalo, José P. Fernández Álvarez, Heidi A. Kuzma, and César O. Menéndez Pérez. “PSO: A Powerful Algorithm to Solve Geophysical Inverse Problems.” Journal of Applied Geophysics 71, no. 1 (May 2010): 13–25. doi:10.1016/j.jappgeo.2010.02.001.
Poli, Riccardo. “Analysis of the Publications on the Applications of Particle Swarm Optimisation.” Journal of Artificial Evolution and Applications (2008): 1–10. doi:10.1155/2008/685175.
Xiang, Yan, Shu-yan Fu, Kai Zhu, Hui Yuan, and Zhi-yuan Fang. “Seepage Safety Monitoring Model for an Earth Rock Dam Under Influence of High-Impact Typhoons Based on Particle Swarm Optimization Algorithm.” Water Science and Engineering 10, no. 1 (January 2017): 70–77. doi:10.1016/j.wse.2017.03.005.
Chi, Shichun, Shasha Ni, and Zhenping Liu. “Back Analysis of the Permeability Coefficient of a High Core Rockfill Dam Based on a RBF Neural Network Optimized Using the PSO Algorithm.” Mathematical Problems in Engineering 2015 (2015): 1–15. doi:10.1155/2015/124042.
Gamse, Sonja, and Michael Oberguggenberger. “Assessment of Long-Term Coordinate Time Series Using Hydrostatic-Season-Time Model for Rock-Fill Embankment Dam.” Structural Control and Health Monitoring 24, no. 1 (April 6, 2016): e1859. doi:10.1002/stc.1859.
Chen, Yifeng, Chuangbing Zhou, and Yongqing Sheng. “Formulation of Strain-Dependent Hydraulic Conductivity for a Fractured Rock Mass.” International Journal of Rock Mechanics and Mining Sciences 44, no. 7 (October 2007): 981–996. doi:10.1016/j.ijrmms.2006.12.004.
Hamm, Se-Yeong, MoonSu Kim, Jae-Yeol Cheong, Jung-Yul Kim, Moon Son, and Tae-Won Kim. “Relationship Between Hydraulic Conductivity and Fracture Properties Estimated from Packer Tests and Borehole Data in a Fractured Granite.” Engineering Geology 92, no. 1–2 (June 2007): 73–87. doi:10.1016/j.enggeo.2007.03.010.
Manda, Alex K., Stephen B. Mabee, David F. Boutt, and Michele L. Cooke. “A Method of Estimating Bulk Potential Permeability in Fractured-Rock Aquifers Using Field-Derived Fracture Data and Type Curves.” Hydrogeology Journal 21, no. 2 (November 10, 2012): 357–369. doi:10.1007/s10040-012-0919-2.
Ren, Jie, Zhen-zhong Shen, Jie Yang, and Chong-zhen Yu. “Back Analysis of the 3D Seepage Problem and Its Engineering Applications.” Environmental Earth Sciences 75, no. 2 (January 2016). doi:10.1007/s12665-015-4837-1.
Turkmen, Sedat. “Treatment of the Seepage Problems at the Kalecik Dam (Turkey).” Engineering Geology 68, no. 3–4 (March 2003): 159–169. doi:10.1016/s0013-7952(02)00225-9.
Jiang, Zhenxiang, and Jinping He. “Detection Model for Seepage Behavior of Earth Dams Based on Data Mining.” Mathematical Problems in Engineering 2018 (2018): 1–11. doi:10.1155/2018/8191802.
Zhang, Jiafa, Jinlong Wang, and Haodong Cui. “Causes of the Abnormal Seepage Field in a Dam with Asphaltic Concrete Core.” Journal of Earth Science 27, no. 1 (February 2016): 74–82. doi:10.1007/s12583-016-0623-6.
Tarantola, Albert. “Inverse Problem Theory and Methods for Model Parameter Estimation” (January 2005). doi:10.1137/1.9780898717921.
Lingireddy, Srinivasa. “Aquifer Parameter Estimation Using Genetic Algorithms and Neural Networks.” Civil Engineering and Environmental Systems 15, no. 2 (March 1998): 125–144. doi:10.1080/02630259808970234.
Yeh, William W-G. “Review of Parameter Identification Procedures in Groundwater Hydrology: The Inverse Problem.” Water Resources Research 22, no. 2 (February 1986): 95–108. doi:10.1029/wr022i002p00095.
Alcolea, Andrés, Jesús Carrera, and Agustín Medina. “Pilot Points Method Incorporating Prior Information for Solving the Groundwater Flow Inverse Problem.” Advances in Water Resources 29, no. 11 (November 2006): 1678–1689. doi:10.1016/j.advwatres.2005.12.009.
Bastani, Mehrdad, Majid Kholghi, and Gholam Reza Rakhshandehroo. “Inverse Modeling of Variable-Density Groundwater Flow in a Semi-Arid Area in Iran Using a Genetic Algorithm.” Hydrogeology Journal 18, no. 5 (March 30, 2010): 1191–1203. doi:10.1007/s10040-010-0599-8.
Karpouzos, D. K., F. Delay, K. L. Katsifarakis, and G. de Marsily. “A Multipopulation Genetic Algorithm to Solve the Inverse Problem in Hydrogeology.” Water Resources Research 37, no. 9 (September 2001): 2291–2302. doi:10.1029/2000wr900411.
Samuel, Manoj P., and Madan K. Jha. "Estimation of aquifer parameters from pumping test data by genetic algorithm optimization technique." Journal of irrigation and drainage engineering 129, no. 5 (2003): 348-359. doi:10.1061/(asce)0733-9437(2003)129:5(348).
Dietrich, C R, and G N Newsam. “Sufficient Conditions for Identifying Transmissivity in a Confined Aquifer.” Inverse Problems 6, no. 3 (June 1, 1990): L21–L28. doi:10.1088/0266-5611/6/3/002.
Jing, L. H., S. C. Duan, and S. Q. Yang. "Application of seepage back analysis to engineering design." Chinese Journal of Rock Mechanics and Engineering 26 (2007): 4503-4509.
Chang, Ya-Chi, Hund-Der Yeh, and Yen-Chen Huang. “Determination of the Parameter Pattern and Values for a One-Dimensional Multi-Zone Unconfined Aquifer.” Hydrogeology Journal 16, no. 2 (October 19, 2007): 205–214. doi:10.1007/s10040-007-0228-3.
Garcia, Luis A., and Abdalla Shigidi. “Using Neural Networks for Parameter Estimation in Ground Water.” Journal of Hydrology 318, no. 1–4 (March 2006): 215–231. doi:10.1016/j.jhydrol.2005.05.028.
Virbulis, Janis, Uldis Bethers, Tomas Saks, Juris Sennikovs, and Andrejs Timuhins. “Hydrogeological Model of the Baltic Artesian Basin.” Hydrogeology Journal 21, no. 4 (March 26, 2013): 845–862. doi:10.1007/s10040-013-0970-7.
Woodbury, Allan D., and Tadeusz J. Ulrych. “A Full-Bayesian Approach to the Groundwater Inverse Problem for Steady State Flow.” Water Resources Research 36, no. 8 (August 2000): 2081–2093. doi:10.1029/2000wr900086.
Dai, Zhenxue, Elizabeth Keating, Carl Gable, Daniel Levitt, Jeff Heikoop, and Ardyth Simmons. “Stepwise Inversion of a Groundwater Flow Model with Multi-Scale Observation Data.” Hydrogeology Journal 18, no. 3 (November 10, 2009): 607–624. doi:10.1007/s10040-009-0543-y.
Gong, Wenyin, Zhihua Cai, and Liangxiao Jiang. “Enhancing the Performance of Differential Evolution Using Orthogonal Design Method.” Applied Mathematics and Computation 206, no. 1 (December 2008): 56–69. doi:10.1016/j.amc.2008.08.053.
Coppola Jr, Emery, Ferenc Szidarovszky, Mary Poulton, and Emmanuel Charles. "Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions." Journal of Hydrologic Engineering 8, no. 6 (2003): 348-360. doi:10.1061/(asce)1084-0699(2003)8:6(348).
Neaupane, K. “Use of Backpropagation Neural Network for Landslide Monitoring: a Case Study in the Higher Himalaya.” Engineering Geology (May 2004): 213-226. doi:10.1016/s0013-7952(04)00080-8.
Kurtulus, Bedri, and Moumtaz Razack. “Evaluation of the Ability of an Artificial Neural Network Model to Simulate the Input-Output Responses of a Large Karstic Aquifer: The La Rochefoucauld Aquifer (Charente, France).” Hydrogeology Journal 15, no. 2 (September 23, 2006): 241–254. doi:10.1007/s10040-006-0077-5.
Rafiai, H., A. Jafari, and A. Mahmoudi. “Application of ANN-Based Failure Criteria to Rocks under Polyaxial Stress Conditions.” International Journal of Rock Mechanics and Mining Sciences 59 (April 2013): 42–49. doi:10.1016/j.ijrmms.2012.12.003.
Ko, Nak-Youl, Sung-Hoon Ji, Yong-Kwon Koh, and Jong-Won Choi. “Consideration of Boreholes in Modeling of the Regional-Scale Groundwater Flow in a Fractured Rock.” Engineering Geology 149–150 (November 2012): 13–21. doi:10.1016/j.enggeo.2012.08.008.
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