Predicting land subsidence rate by estimating aquifer hydraulic parameters, obtained from aquifer-test data and optimization algorithms in the district 19 of Tehran

Arjomandi, M.; Saremi, A.; Sarraf, A. P.; Sedghi, H.; Roustaei, M.

doi:10.22071/gsj.2018.63740

Document Type : Original Research Paper

Authors

¹ Ph.D. Student, Department of Water Sciences and Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

² Assistant Professor, Department of Water Sciences and Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

³ Assistant Professor, Department of Civil Engineering, Islamic Azad University, Roudehen Branch, Roudehen, Iran

⁴ Professor, Department of Water Sciences and Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

⁵ Ph.D., Geological Survey of Iran, Tehran, Iran

https://doi.org/10.22071/gsj.2018.63740

Abstract

Groundwater modeling, land subsidence hazards and proper management of groundwater resources of the alluvial aquifer in the district 19 of Tehran, south of Tehran Plain need precise estimation of aquifer hydraulic parameters. Besides, traditional techniques and usual graphical methods have been approximate, expensive and time-consuming. In this paper, ten sets of aquifer test data were selected; moreover, these data were recorded in a well located in district 19 of Tehran, southwest of Tehran Plain during five years (2008 to 2012). For solving the previous methods’ problems, three computer codes have been developed to optimize aquifer parameters using three optimization approaches on the other hand, two kinds of genetic algorithms and a multi-elitist particle swarm optimization (MEPSO) which avoid getting stuck in local optima and save time. The efficacy and efficiency of the developed codes have been examined using ten sets of aquifer test data of a confined aquifer, and then their results have been compared with the results obtained by the graphical approach using AquiferTest software. Based on the fitness function, i.e. sum of square errors, the MEPSO and the GAs in descending order are more reliable for estimating the parameters contrast with the graphical method. Furthermore, the sensitivity analysis of the parameters during the performance of the optimization approaches has authenticated that the results obtained are enough precise and reliable. Then an equation has been presented according to the amounts of hydraulic conductivity which have been obtained using MEPSO during the years and the amounts of land subsidence rates which have been obtained using geodetic measurement methods to predict the amounts of land subsidence rates through the time when the amount of hydraulic conductivity will reach to Ultimately, based on the equation, after 30 years the amount of hydraulic conductivity will reach to and the total amount of land subsidence will be 0.5213 m from 2008 to 2038 . Moreover, land subsidence rates’ data obtained from interferometry synthetic aperture radar (InSAR) have confirmed the accuracy of the equations.

Keywords

20.1001.1.10237429.1397.27.107.4.5

References

References

Aghanabati, A., 1993- Geological map of Tehran. Scale 1:100000, ed: Geological Survey of Iran (GSI).

Abd-Elhamid, H. F. and Javadi, A. A., 2011- A cost-effective method to control seawater intrusion in coastal aquifers, Water resources management, vol. 25, pp. 2755-2780.

Bateni, S., Mortazavi-Naeini, M., Ataie-Ashtiani, B., Jeng, D. and Khanbilvardi, R., 2015- Evaluation of methods for estimating aquifer hydraulic parameters, Applied Soft Computing, vol. 28, pp. 541-549.

Das, S., Abraham, A., and Konar, A., 2008- Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm, Pattern recognition letters, vol. 29, pp. 688-699.

Davis, L., 1991- The handbook of genetic algorithms Van Nostrand Reingold, New York.

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., 2002- A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE transactions on evolutionary computation, vol. 6, pp. 182-197.

Eberhart, R. C., 2001- Optimization, Particle Swarm Developments, Applications and Resources," ed: Purdue School of Engineering and Technology 799 West Michigan Street Indianapolis, IN 46202 USA, pp. 1-145.

Erban, L. E., Gorelick, S. M., and Zebker, H. A., 2014- groundwater extraction, land subsidence and sea-level rise in the Mekong Delta, Vietnam, pp. 1-5.

Ferretti, A., Monti-Guarnieri, A., Prati, C., Rocca, F. and Massonet, D., 2007- InSAR principles-guidelines for SAR interferometry processing and interpretation vol. 19, pp. 1-106.

Gentry, R. W., Camp, C. V. and Anderson, J. L., 2001- Use of GA to determine areas of accretionto semiconfined aquifer, Journal of Hydraulic Engineering, vol. 127, pp. 738-746.

Gershenwald, J. E., Colome, M. I., Lee, J. E., Mansfield, P. F., Tseng, C. H. and Lee, J. J., 1998- Patterns of recurrence following a negative sentinel lymph node biopsy in 243 patients with stage I or II melanoma," Journal of clinical oncology, vol. 16, pp. 2253-2260

Golberg, D. E., 1989- Genetic algorithms in search, optimization, and machine learning, Addion wesley, vol. 1989, pp. 102.

Goulter, I., 1992- Systems analysis in water-distribution network design: From theory to practice," Journal of Water Resources Planning and Management, vol. 118, pp. 238-248.

Gwo, J. P., 2001- In search of preferential flow paths in structured porous media using a simple genetic algorithm- Water Resources Research, vol. 37, pp. 1589-1601.

http://www.aqtesolv.com.

Khu, S. T., Liong, S. Y., Babovic, V., Madsen, H. and Muttil, N., 2001- Genetic programming and its application in real‐time runoff forecasting1, JAWRA Journal of the American Water Resources Association, vol. 37, pp. 439-451.

Kresic, N., 1996- Hydrogeology and groundwater modeling: CRC press, pp. 86-183.

Kresic, N., 2006- Hydrogeology and groundwater modeling: CRC press, pp. 1-53.

Mahmoodpour, M., 2015- Numerical simulation and prediction of regional land subsidence caused by groundwater exploitation in the southwest plain of Tehran, iran, pp. 6-28.

Meier, R. W. and Barkdoll, B. D., 2000- Sampling design for network model calibration using genetic algorithms, Journal of Water Resources Planning and Management, vol. 126, pp. 245-250.

Molaei, M., Meshkat, T., Akbari, K., Nazarjani, M., Esmailzadeh Nasiri, M. and Hesami, 2016- Report of water resources’ management of Tehran, water resources’ management Institute of Iran, chapters 1-6.

Poli, R., Kennedy, J. and Blackwell, T., 2007- Particle swarm optimization, Swarm intelligence, vol. 1, pp. 33-57.

Prasad, K. L. and Rastogi, A., 2001- Estimating net aquifer recharge and zonal hydraulic conductivity values for Mahi Right Bank Canal project area, India by genetic algorithm, Journal of Hydrology, vol. 243, pp. 149-161.

Samuel, M. P. and Jha, M. K., 2003- Estimation of aquifer parameters from pumping test data by genetic algorithm optimization technique," Journal of irrigation and drainage engineering, vol. 129, pp. 348-359.

Shang, C., Srinivasan D. and Reindl, T., 2016- An improved particle swarm optimisation algorithm applied to battery sizing for stand-alone hybrid power systems, International Journal of Electrical Power & Energy Systems, vol. 74, pp. 104-117.

Simpson, A. R., Dandy, G. C. and Murphy, L. J., 1994- Genetic algorithms compared to other techniques for pipe optimization," Journal of water resources planning and management, vol. 120, pp. 423-443.

Skempton, A. W., 1984- The pore-pressure coefficients A and B. Soil Mechanics Journal, Thomas Telford Publishing, pp. 65-69.

Sun, H., Grandstaff, D. and Shagam, R., 1999- Land subsidence due to groundwater withdrawal: potential damage of subsidence and sea level rise in southern New Jersey, USA, Environmental Geology, vol. 37, pp. 290-296.

Sun, N. Z., 1994- Inverse Problem in Groundwater Modeling, Kluwer Academic Publishers, ed: Boston, pp. 1-24.

Taylor, J. H. 1985- Controlled traffic: A spin-off of soil dynamics research, Information Systems Division, National Agricultural Library, pp. 1-186.

Theis, C. V., 1935- The relation between the lowering of the Piezometric surface and the rate and duration of discharge of a well using ground-water storage, Eos, Transactions American Geophysical Union, vol. 16, pp. 519-524.

Wardlaw, R. and Sharif, M., 1999- Evaluation of genetic algorithms for optimal reservoir system operation, Journal of water resources planning and management, vol. 125, pp. 25-33

Scientific Quarterly Journal of Geosciences

Predicting land subsidence rate by estimating aquifer hydraulic parameters, obtained from aquifer-test data and optimization algorithms in the district 19 of Tehran

References

References

Volume 27, Issue 107
Volume 26, Issue 107, spring 2018
June 2018
Pages 17-26

Predicting land subsidence rate by estimating aquifer hydraulic parameters, obtained from aquifer-test data and optimization algorithms in the district 19 of Tehran

References

References

Volume 27, Issue 107Volume 26, Issue 107, spring 2018June 2018Pages 17-26

Volume 27, Issue 107
Volume 26, Issue 107, spring 2018
June 2018
Pages 17-26