Abstract
One of the most common tasks of groundwater engineers is to estimate aquifer parameters from transient time-drawdown data which are measured during the execution of pumping tests. The classical method consists in the manual superimposition of the observed time-drawdown data over a type-curve, and the appreciation of the goodness-of-fit is left to the visual inspection of the engineer. The present paper proposes a computerized method based on a new hybrid algorithm named CSARao-1. It combines two recent metaheuristic algorithms: the crow search algorithm (CSA) and Rao-1 algorithm for the optimal aquifer parameters estimation, which inherits the advantages of both algorithms. The CSARao-1 hybrid algorithm along with CSA and Rao-1 algorithm was applied in conjunction with both the Theis solution to analyze time-drawdown datasets coming from confined aquifers systems and the Hantush and Jacob solution to analyze datasets coming from leaky aquifers systems. The proposed approach was coded in FORTRAN programming language and evaluated on fourteen time-drawdown datasets coming from different confined and leaky aquifers systems. The results obtained using CSARao-1 hybrid algorithm were compared to those obtained by applying CSA and Rao-1 algorithm separately, and to those recently published. Globally, the proposed CSARao-1 hybrid algorithm was found to exhibit more accuracy, better robustness and higher rate of convergence over the analyzed transient time-drawdown datasets.
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References
Allaoui M, Ahiod B, El Yafrani M (2018) A hybrid crow search algorithm for solving the DNA fragment assembly problem. Expert Syst Appl 102:44–56
Arora S, Singh H, Sharma M, Sharma S, Anand P (2019) A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. IEEE Access 7:26343–26361. https://doi.org/10.1109/ACCESS.2019.2897325
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001
Ayvaz MT, Gurarslan G (2018) Identification of the aquifer parameters from pumping test data by using a hybrid optimization approach. In: La Loggia G, Freni G, Puleo V, De Marchis M (eds) HIC 2018. 13th international conference on hydroinformatics. EasyChair pp 147–154. https://doi.org/10.29007/11v5
Ayvaz MT, Gurarslan G (2019) A hybrid optimization approach for parameter estimation of confined and leaky confined aquifers. Water Supply 19:2359–2366
Banadkooki FB, Adamowski J, Singh VP, Ehteram M, Karami H, Mousavi SF, Farzin S, EL-Shafie A (2020) Crow algorithm for irrigation management: a case study. Water Resour Manag 34:1021–1045. https://doi.org/10.1007/s11269-020-02488-6
Bateni SM, Mortazavi-Naeini M, Ataie-Ashtiani B, Jeng DS, Khanbilvardi R (2015) Evaluation of methods for estimating aquifer hydraulic parameters. Appl Soft Comput J 28:541–549. https://doi.org/10.1016/j.asoc.2014.12.022
Batu V (1998) Aquifer hydraulics: a comprehensive guide to hydrogeologic data analysis. Wiley, USA
Ch S, Mathur S (2012) Particle swarm optimization trained neural network for aquifer parameter estimation. KSCE J Civ Eng 16:298–307. https://doi.org/10.1007/s12205-012-1452-5
Delnaz A, Rakhshandehroo G, Nikoo MR (2020) Confined aquifer’s hydraulic parameters estimation by a generalized regression neural network. Iran J Sci Technol Trans Civ Eng 44:259–269. https://doi.org/10.1007/s40996-019-00238-2
Dey B, Bhattacharyya B, Srivastava A, Shivam K (2020) Solving energy management of renewable integrated microgrid systems using crow search algorithm. Soft Comput 24:10433–10454. https://doi.org/10.1007/s00500-019-04553-8
El-Ashmawi WH, Ali AF, Slowik A (2021) Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system. Neural Comput Appl 33:7145–7164. https://doi.org/10.1007/s00521-020-05482-6
Farh HM, Al-Shaalan AM, Eltamaly AM, Al-Shammaa AA (2020) A novel crow search algorithm auto-drive PSO for optimal allocation and sizing of renewable distributed generation. IEEE Access 8:2780–27820. https://doi.org/10.1109/ACCESS.2020.2968462
Flores L, Bailey RT (2019) Review: revisiting the Theis solution derivation to enhance understanding and application. Hydrogeol J. https://doi.org/10.1007/s10040-018-1843-x
Gholami J, Mardukhi F, Zawbaa HM (2021) An improved crow search algorithm for solving numerical optimization functions. Soft Comput 25:9441–9454. https://doi.org/10.1007/s00500-021-05827-w
Gupta D, Sundaram S, Rodrigues JJ, Khanna A (2019) An improved fault detection crow search algorithm for wireless sensor network. Int J Commun Syst. https://doi.org/10.1002/dac.4136
Han X, Xu Q, Yue L, Dong Y, Xie G, Xu X (2020) An improved crow search algorithm based on spiral search mechanism for solving numerical and engineering optimization problems. IEEE Access 8:92363–92382. https://doi.org/10.1109/ACCESS.2020.2980300
Hantush MS, Jacob CE (1955) Non-steady radial flow in an infinite leaky aquifer. Trans Am Geophys Union 36(1):95–100. https://doi.org/10.1029/TR036i001p00095
Huang YC, Hund-Der Y, Lin YC (2008) A computer method based on simulated annealing to identify aquifer parameters using pumping-test data. Int J Numer Anal Methods Geomech 32:235–249. https://doi.org/10.1002/nag.623
Kaur A, Kaur R, Sondhi S (2020) CSA based PID controller design technique for optimizing various integral errors. In: 2020 10th international conference on cloud computing, data science & engineering (confuence). IEEE, pp 55–62
Khalilpourazari S, Pasandideh SHR (2020) Sine–cosine crow search algorithm: theory and applications. Neural Comput Appl 32:7725–7742. https://doi.org/10.1007/s00521-019-04530-0
Kresic N (1997) Quantitative solutions in hydrogeology and groundwater modeling, 1st edn. CRC Press, Boca Raton, FL, USA
Kruseman GP, de Ridder NA (2000) Analysis and evaluation of pumping test data, 2nd edn. International Institute for Land Reclamation and Improvement, The Netherlands
Li Q, Liu SY, Yang XS (2020) Influence of initialization on the performance of metaheuristic optimizers. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020.106193
Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul 8(1):3–30
Meraihi Y, Gabis AB, Ramdane-Cherif A, Acheli D (2021) A comprehensive survey of Crow search algorithm and its applications. Artif Intell Rev 54:2669–2716. https://doi.org/10.1007/s10462-020-09911-9
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Naderi M (2019) Estimating confined aquifer parameters using a simple derivative-based method. Heliyon. https://doi.org/10.1016/j.heliyon.2019.e02657
Omar A, Hasanien HM, Elgendy MA, Badr MA (2017) Identifcation of the photovoltaic model parameters using the crow search algorithm. J Eng 13:1570–1575
Ouadfel S, AbdElaziz M (2020) Enhanced crow search algorithm for feature selection. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113572
Rajesh M, Kashyap D, Hari Prasad KS (2010) Estimation of unconfined aquifer parameters by genetic algorithms. Hydrol Sci J 55:403–413. https://doi.org/10.1080/02626661003738167
Rao RV (2020) Rao algorithms: three metaphor-less simple algorithms for solving optimization problems. Int J Ind Eng Comput 11:107–130. https://doi.org/10.5267/j.ijiec.2019.6.002
Rao RV, Keesari HS (2021) A self-adaptive population Rao algorithm for optimization of selected bio-energy systems. J Comput Des Eng 8:69–96. https://doi.org/10.1093/jcde/qwaa063
Rao RV, Pawar RB (2020) Constrained design optimization of selected mechanical system components using Rao algorithms. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020.106141
Samuel MP, Jha MK (2003) Estimation of aquifer parameters from pumping test data by genetic algorithm optimization technique. J Irrig Drain Eng 129:348–359. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:5(348)
Şen Z (1995) Applied hydrogeology for scientists and engineers. CRC Press, USA
Sörensen K (2015) Metaheuristics-the metaphor exposed. Int Trans Oper Res 22:3–18. https://doi.org/10.1111/itor.12001
Sultana N, Hossain SMZ, Alam MS, Hashish MMA, Islam MS (2020) An experimental investigation and modeling approach of response surface methodology coupled with crow search algorithm for optimizing the properties of jute fiber reinforced concrete. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.118216
Sun Q, Wang Y, Jiang Y, Shao L (2018) Non-invasive condition monitoring for boost converter based on crow search algorithm. J Intell Fuzzy Syst 34(6):3661–3670. https://doi.org/10.3233/JIFS-169541
Suyanto S, Wibowo AT, Faraby SA, Saadah S, Rismala R (2021) Evolutionary rao algorithm. J Comput Sci 53:101368. https://doi.org/10.1016/j.jocs.2021.101368
Tabari MMR, Azari T, Dehghan V (2021) A supervised committee neural network for the determination of aquifer parameters: a case study of Katasbes aquifer in Shiraz plain. Iran Soft Comput 25:4785–4798. https://doi.org/10.1007/s00500-020-05487-2
Tadj W, Chettih M, Mouattah K (2018) Automatic interpretation of pumping tests data using metaheuristics. Arab J Geosci. https://doi.org/10.1007/s12517-018-3730-0
Theis CV (1935) The relation between the lowering of the Piezometric surface and the rate and duration of discharge of a well using ground-water storage. Trans Am Geophys Union 16:519–524. https://doi.org/10.1029/TR016i002p00519
Todd DK, Mays LW (2005) Groundwater hydrology. Wiley, Hoboken
Tseng PH, Lee TC (1998) Numerical evaluation of exponential integral: theis well function approximation. J Hydrol 205:38–51. https://doi.org/10.1016/S0022-1694(97)00134-0
Upadhyay P, Chhabra JK (2019) Kapur’s entropy based optimal multilevel image segmentation using crow search algorithm. Appl Soft Comput 97:105522
Wang L, Wang Z, Liang H, Huang C (2020) Parameter estimation of photovoltaic cell model with Rao-1 algorithm. Optik (stuttg). https://doi.org/10.1016/j.ijleo.2019.163846
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82. https://doi.org/10.1109/4235.585893
Wu H, Wu P, Xu K, Li F (2020) Finite element model updating using crow search algorithm with Levy flight. Int J Numer Methods Eng 121:2916–2928. https://doi.org/10.1002/nme.6338
WWAP (United Nations World Water Assessment Programme) (2015) The united nations world water development report 2015: Water for a sustainable world. UNESCO, Paris
Yeh H-D, Lin Y-C, Huang YC (2007) Parameter identification for leaky aquifers using global optimization methods. Hydrol Process 21:862–872. https://doi.org/10.1002/hyp.6274
Zamani H, Nadimi-Shahraki MH, Gandomi AH (2019) CCSA: conscious neighborhood-based crow search algorithm for solving global optimization problems. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2019.105583
Zhao W, Wang L, Zhang Z (2019) Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04452-x
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Tadj, W., Chettih, M. & Mouattah, K. A new hybrid algorithm for estimating confined and leaky aquifers parameters from transient time-drawdown data. Soft Comput 25, 15463–15476 (2021). https://doi.org/10.1007/s00500-021-06224-z
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DOI: https://doi.org/10.1007/s00500-021-06224-z