Abstract
In the inverse groundwater modelling problems, the objective functions generally used contain several local minima which render the conventional gradient-based optimization unsuitable for such problems. The recently used individual population-based evolutionary methods such as differential evolution (DE) algorithm and particle swarm optimization (PSO) are often observed to get stuck into sub-optimal solution. In this study to address this issue, a hybrid- metaheuristic Differential Evolution- Particle Swarm Optimization (DE-PSO) is proposed to obtain aquifer parameters. PSO introduces a perturbation in each generation to increase the diversity in the population of DE to improve its fitness value. The developed hybrid DE-PSO optimization is coupled with finite element method (FEM) based simulator to get a simulation-optimization (SO) model. Initially, the proposed SO model is tested on a synthetic irregular domain problem to estimate aquifer transmissivity values which are compared with available zonation pattern values. Later, the SO model is applied to the Mahi Right Bank Canal (MRBC) heterogeneous unconfined aquifer system and the optimally obtained results are compared with the DE, PSO and genetic algorithm (GA) methods respectively. The performance of the hybrid DE-PSO model is also tested using various fit- independent statistics for the reliability and accuracy. The results of this study show that the hybrid-metaheuristic based DE-PSO optimization algorithm is an efficient and robust tool for inverse groundwater problem of estimating the aquifer parameters.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aral MM, Guan J, Maslia ML (2001) Identification of contaminant source location and release history in aquifers. Journal of Hydrologic Engineering 6(3):225–234. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:3(225)
Chiu Y-C (2014) Application of differential evolutionary optimization methodology for parameter structure identification in groundwater modeling. Hydrogeology Journal 22(8):1731–1748. https://doi.org/10.1007/s10040-014-1172-7
Cyriac R, Rastogi AK (2016) Optimization of pumping policy using coupled finite element-particle swarm optimization modelling. ISH Journal of Hydraulic Engineering 22(1):88–99. https://doi.org/10.1080/09715010.2015.1080126
Du K-L, Swamy MNS (2016) Search and optimization by Metaheuristics. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-41192-7
R, Eberhart, J, Kennedy. 1995. A new optimizer using particle swarm theory. In MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human ScienceIEEE; 39–43. DOI: https://doi.org/10.1109/MHS.1995.494215
Elshall AS, Pham HV, Tsai FT-C, Yan L, Ye M (2015) Parallel inverse modeling and uncertainty quantification for computationally demanding groundwater-flow models using covariance matrix adaptation. Journal of Hydrologic Engineering 20(8):4014087. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001126
Gehman CL, Harry DL, Sanford WE, Stednick JD, Beckman NA (2009) Estimating specific yield and storage change in an unconfined aquifer using temporal gravity surveys. Water Resources Research 45(4). https://doi.org/10.1029/2007WR006096
X, He, JJ, Liu. 2009. Aquifer Parameter Identification with Ant Colony Optimization Algorithm. In 2009 International Workshop on Intelligent Systems and ApplicationsIEEE; 1–4. DOI: https://doi.org/10.1109/IWISA.2009.5072758
MC, Hill. 2000. Methods and guidelines for effective model calibration. In Building PartnershipsAmerican Society of Civil Engineers: Reston, VA; 1–10. DOI: https://doi.org/10.1061/40517(2000)18
Hill MC, Tiedeman CR (2006) Effective groundwater model calibration: with analysis of data, sensitivities, predictions, and uncertainty. John Wiley & Sons
IARI Research Bulletin-42 (1983) Resource analysis and plane for efficient water management- a case study of Mahi River Bank Canal command area, Gujarat, New Delhi
Jha MK, Sahoo S (2015) Efficacy of neural network and genetic algorithm techniques in simulating spatio-temporal fluctuations of groundwater. Hydrological Processes 29(5):671–691. https://doi.org/10.1002/hyp.10166
Jiang Y, Liu C, Huang C, Wu X (2010) Improved particle swarm algorithm for hydrological parameter optimization. Applied Mathematics and Computation 217(7):3207–3215. https://doi.org/10.1016/j.amc.2010.08.053
Kannan S, Slochanal SMR, Subbaraj P, Padhy NP (2004) Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electric Power Systems Research 70(3):203–210. https://doi.org/10.1016/j.epsr.2003.12.009
Keidser A, Rosbjerg D (1991) A comparison of four inverse approaches to groundwater flow and transport parameter identification. Water Resources Research 27(9):2219–2232. https://doi.org/10.1029/91WR00990
Kumar D, Ch S, Mathur S, Adamowski J (2015) Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing. Journal of Water and Land Development 27(1):29–40. https://doi.org/10.1515/jwld-2015-0022
Lakshmi Prasad K, Rastogi AK (2001) Estimating net aquifer recharge and zonal hydraulic conductivity values for Mahi right Bank Canal project area, India by genetic algorithm. Journal of Hydrology 243(3–4):149–161. https://doi.org/10.1016/S0022-1694(00)00364-4
Kumar MG, Sayeed M (2005) Hybrid genetic algorithm—local search methods for solving groundwater source identification inverse problems. Journal of Water Resources Planning and Management 131(1):45–57. https://doi.org/10.1061/(ASCE)0733-9496(2005)131:1(45)
Michael AM (2009) Irrigation: theory and practice. Vikas Publishing House Pvt Limited
Price K, Storn RM, Lampinen JA (2005) Differential Evolution. Springer-Verlag, Berlin/Heidelberg. https://doi.org/10.1007/3-540-31306-0
AK, Rastogi (2012) Numerical groundwater hydrology. Penram international publishing (I) pvt. ltd
Rastogi AK, Huggi VP (2009) Parameter assessment in flow through porous media. ISH Journal of Hydraulic Engineering15(sup1):272–296. https://doi.org/10.1080/09715010.2009.10514980
Sondhi SK, Rao NH, Sarma PBS (1989) Assessment of groundwater potential for conjunctive water use in a large irrigation project in India. Journal of Hydrology 107(1–4):283–295. https://doi.org/10.1016/0022-1694(89)90062-0
Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4):341–359. https://doi.org/10.1023/A:1008202821328
Sun N-Z (1999) Inverse problems in groundwater modeling. Springer Netherlands, Dordrecht. https://doi.org/10.1007/978-94-017-1970-4
Sun N-Z, Yeh WW-G (1990) Coupled inverse problems in groundwater modeling: 1. Sensitivity analysis and parameter identification. Water Resources Research 26(10):2507–2525. https://doi.org/10.1029/WR026i010p02507
Wu Y, Lee W, Chien C (2011) Modified the performance of differential evolution algorithm with dual evolution strategy. In: In International Conference on Machine Learning and ComputingIACSIT press: Singa, pp 57–63
Yao L, Guo Y (2014) Hybrid algorithm for parameter estimation of the groundwater flow model with an improved genetic algorithm and gauss-Newton method. Journal of Hydrologic Engineering 19(3):482–494. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000823
Zhou H, Gómez-Hernández JJ, Li L (2014) Inverse methods in hydrogeology: evolution and recent trends. Advances in Water Resources 63:22–37. https://doi.org/10.1016/j.advwatres.2013.10.014
Acknowledgements
We are very much thankful to Gujarat Water Resources Development Corporation (GWRDC), Gandhinagar and Mahi Irrigation Circle, Nadiad for the necessary field data of the MRBC project area.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Patel, S., Eldho, T. & Rastogi, A. Hybrid-Metaheuristics Based Inverse Groundwater Modelling to Estimate Hydraulic Conductivity in a Nonlinear Real-Field Large Aquifer System. Water Resour Manage 34, 2011–2028 (2020). https://doi.org/10.1007/s11269-020-02540-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-020-02540-5