تخمین پارامترهای هیدرولیکی سفره‌های تحت فشار بوسیله تکنیک بهینه سازی الگوریتم ژنتیک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار/ گروه زمین شناسی دانشگاه تبریز.

2 استادیار /گروه مهندسی عمران آب دانشگاه تبریز.

3 دانشجوی کارشناسی ارشد /هیدروژئولوژی دانشگاه تبریز.

چکیده

توسعه پایدار و بهره برداری بهینه از منابع آب زیرزمینی بستگی به صحت تعیین پارامترهای هیدرولیکی آبخوان‌ها دارد. روشهای متفاوتی برای تعیین پارامترهای هیدرولیکی آبخوان وجود دارد. یکی از روشهای کلاسیک جهت تخمین این پارامترها آنالیز داده‌های آزمایش پمپاژ با روشهای گرافیکی است. امروزه روشهای بهینه‌سازی احتمالاتی از قبیل شبیه سازی آنیله، الگوریتم ژنتیک(GA1) و... که برپایه قوانین تکامل بیولوژیکی استوار هستند، بواسطه قابلیتهای فراوان با اقبال مجامع تحقیقاتی روبرو شده اند. در این مقاله کارایی روش GA در تخمین پارامترهای هیدرولیکی سفره‌های تحت فشار از داده‌های آزمایش پمپاژ مورد ارزیابی قرار گرفته است. بدین منظور با استفاده از GA پارامترهای چهار سفره تحت فشار برآورد و با نتایج حاصل از روشهای گرافیکی مقایسه گردیده است. مقایسه نتایج حاصله نشان می‌دهند که تکنیک هوشمند GA روشی کارا، قابل اعتماد و قوی جهت تخمین پارامترهای هیدرولیکی سفره تحت فشار می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of Hydraulic Parameters of Confined Aquifers Using Genetic Algorithm Optimization Technique

نویسندگان [English]

  • A Asghari Moghaddam 1
  • V Norani 2
  • M Kord 3
1 Associate professor, geology department, University of Tabriz
2 Assistant professor, water engineering department, University of Tabriz
3 MSc. student, geology department, University of Tabriz
چکیده [English]

Sustainable development and optimized exploitation of the groundwater resources depend on accurate estimation of aquifer hydraulic parameters. Different methods exist for estimation of hydraulic parameters of aquifers. One of the classic methods for estimating these parameters is analyzing the pumping test data by graphical methods. Nowadays, probabilistic optimization methods, i.e. simulated annealing and genetic algorithm (GA), based on evolution rules, are took into attentions due to their high abilities. In this article, the efficiency of the GA is assessed in estimating confined aquifer parameters. For this purpose, hydraulic parameters of four confined aquifers are calculated by using GA and they are compared with results of graphical methods. The results indicate that intelligent GA technique is efficient, reliable and powerful method for estimation of confined aquifers hydraulic parameters.

کلیدواژه‌ها [English]

  • hydraulic parameters
  • Pumping test
  • optimization
  • Genetic algorithm
  • Graphical method
Abbaspour, K. C., van Genuchter, M. T., Schulin, R. and Schlappi, E. (1997), “A sequential uncertainty domain inverse procedure for estimating subsurface flow and transport parameters.” Water Resour. Res., 33(8): pp. 1879-1892.
Adeli, H. and Cheng, N. T. (1993), “Integrated genetic algorithm for optimization of truss structures.” J. aerospac. Eng., 6(4), pp. 315-328.
Ayvaz, T. M. (2007), “Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm.” Advances in Water Res., 30(11), pp. 2326-2338.
Ayvaz, T. M., Karahan, H. and Aral, M. M. (2007), “Aquifer parameter and zone structure estimation using kernel-based fuzzy c-means clustering and genetic algorithm.” J. Hydrol., 343( 3-4), pp. 240-253.
Aziz, A. R. A. and Wong, K.V. (1992), “A neural-network approach to the determination of aquifer parameters.” Ground Water, 30 (2), pp. 164–166.
Balkhair, K.S. (2002), “Aquifer parameters determination for large diameter wells using neural network approach.” J. Hydrol.,265 (1–4), pp. 118–128.
Batu, V. (1998), Aquifer hydraulics: a comprehensive guide to hydrogeologic data analysis, John wiley & sons, inc., 728p.
Carrera, J. and Neuman, S. P. (1986a), “Estimation of aquifer parameters under transient and steady state conditions: 1.Maximum likelihood method incorporating prior information.” Water Resour. Res., 22(2): pp. 199-210.
Chen, T. Y. and Chen, C. j. (1997), ‘‘Improvements of simple genetic algorithm in structural design.’’ International J. Numerical Meths. in Eng., 40, pp. 1323–1334.
Cheng, A. H. D., Halhal, D., Naji, A. and Ouazar, D. (2000), ‘‘Pumping optimization in saltwater-intruded coastal aquifers.’’ Water Resour. Res., 36(8), pp. 2155–2165.
Cooley, R. L. (1977), “A method of estimating parameters and assessing reliability for models of steady state ground water flow 1. Theory and numerical properties.” Water Resour. Res., 13(2): pp. 318-324.
Dagan, G. and Rubin, Y. (1988), “Stochastic identification of recharge, transmissivity, and storativity in aquifer transient flow: A quasi-steady approach.” Water Resour. Res., 24(10): pp. 1698-1710.
Daliakopoulos, I. N., Coulibaly, P. and Tsanis, I.K. (2005), “Groundwater level forecasting using artificial neural networks.” J. Hydrol., 309 (1–4), pp. 229–240.
Davis, L. )1991(, A handbook of genetic algorithms, Van Nostrand, Reinhold, New York.
Freeze, A. R. and Cherry, J. A. (1979), Groundwater, Prentice-Hall, Englewood Cliffs, New Jersey, 603p.
Garcia, L. A. and Shigidi, A. (2006), “Using neural networks for parameter estimation in ground water.” J. Hydrol., 318, pp. 215–231.
Gentry, R. W., Camp, C. V., and Anderson, J. L.(2001). “Use of GA to determine areas of accretion to semiconfined aquifer.” J. Hydraul. Eng., 127(9), pp. 738-746.
Gentry, R. W., Larsen, D. and Ivey, S. (2003), “Efficacy of Genetic Algorithm to investigate Small Scale Aquitard Leakage.” J. Hydraul. Eng., Vol. 129, No. 7.
Giacobbo, F., Marseguerra, M. and Zio, E. (2002), “Solving the inverse problem of parameter estimation by genetic algorithms:  the case of a groundwater  contaminant transport model.” Annals of Nuclear Energy, 29 (8), pp. 967-981.
Goldberg, D. E. (1989), Genetic algorithms in search, optimization and machine learning, Addison-Wesley Publishing Company, New York.
Haupt, R. L.,Haupt, S.E. (2004), Practical genetic algorithms, John Wiley, 253p.
Kruseman, G. P. and De Ridder, N. A. (1983), Analysis and evaluation of pumping test data, ILRT, Wageningen, Netherlands, 200p.
Lin, G.F. and Chen, G.R. (2006), “An improved neural network approach to the determination of aquifer parameters.” J. Hydrol.,316 (1–4), pp. 281–289.
Mitsuo, G. and Cheng, R. (1997), Genetic algorithms and engineering design, John Wiley & Sons, Inc.
Newman, S.P. (1972), “Theory of flow in unconfined aquifers considering delay response of the water table.” Water Resour. Res., 8, pp. 1031-1045.
Prasad, K. L. and Rastogi, A. K. (2001), ‘‘Estimating net aquifer recharge and zonal hydraulic conductivity values for MahiRight BankCanal project area, India by genetic algorithm.’’ J. Hydrol., 243, pp. 149– 161.
Raghunath, H. M. (1987), Ground water, Wiley Eastern Limited, 563p.
Rajasekaran, S. and Vijayalakshmi Pai, G. A. (2005), Neural networks, fuzzy logic, genetic algorithms, Prentice-Hall of India, New Delhi, .
Samani, N., Gohari-Moghadam, M. and Safavi, A. A. (2007), “A simple neural network model for the determination of aquifer  parameters.” J. Hydrol. 340 (1-2), pp. 1-11.
Samuel, M. P. (2002), ‘‘Determination of aquifer and well parameters using genetic algorithm.’’ MTech thesis, Indian Institute of Technology,Kharagpur, India.
Sun, N.-Z. (1994), Inverse problems in groundwater modeling, Kluwer Academic, Dordrecht, the Netherlands.
Tai Kuoa, J., Yi Wanga, Y. and Seng Lungb, W. (2006), “A hybrid neural–genetic algorithm for reservoir water quality management.” Water Res., 40, pp. 1367 –1376.
Todd, D. K., Mays, L. W. (2005), Groundwater hydrology, Wiley, International edition, 636p.
Tseng, P. H. and Lee, T. C. (1998), ‘‘Numerical evaluation of exponential integral: Theis well function approximation.’’ J. Hydrol., 205, pp. 38–51.
 
 
Theis, C.V. (1935), ‘‘The relation between lowering of the piezometric surface and the rate and duration of discharge of a well using groundwater storage.’’ Trans. Amer. Geophys. Union, 2, pp. 519-524.
Zheng, C. and Wang, P. (1996), “Parameter structure identification using tabu search and simulated annealing.” Advances in Water Res., 19(4), pp. 215-224.
Yeh, W. W. (1986), “Review of parameter identification procedures in groundwater hydrology: The inverse problem.” Water Resour. Res., 22(2): pp. 95-108.