Codification of a fuzzy simulation-optimization model for optimal estimation of confined aquifer parameters based on a fuzzy transformation method

Document Type : Research Paper

Authors

• Atefeh Delnaz ¹
• Gholamreza Rakhshandehroo ²
• Mohammad Reza Nikoo ³

¹ M.Sc. Student, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran

² Professor, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran,

³ Associate professor, School of Engineering, Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran

Abstract

For proper groundwater resource management as a vital resource, accurate aquifer parameter determination is required. Existing groundwater management practices, for the sake of simplicity, overlook inherent uncertainties in measurements of pumping test parameters. In the current study a fuzzy simulation-optimization model based on consideration of uncertainties in parameter determination is used. To this regard, the novel fuzzy simulation-optimization model is able to predict the confined aquifer parameter precisely, based on minimizing the deviation between observed and calculated drawdown. The proposed approach is tested on a real pumping test data of a confined aquifer and then the results are compared with graphical solution of Theis method. Comparing several statistical indices based on the results of the proposed method and graphical solution of Theis method, performance of these models are evaluated. As an example, Mean Absolute Relative Error (MARE) of the proposed model and graphical Theis solution is 0.69% and 1.13% respectively which shows the appropriate accurate of the proposed model over the traditional method (graphical Theis solution). Thus, the proposed fuzzy simulation-optimization model may replace the graphical Theis solution. In the second part of the study, by considering pumping rate as an uncertain parameter, a fuzzy optimization model based on fuzzy transformation method is developed. Then, the effect of uncertainty in prediction of aquifer parameters is assessed and ranges of aquifer parameters in various α cuts, are determined. Based on the developed fuzzy results, T is found more sensitive to uncertainty in the pumping rate measurements, as compared to S.

Keywords

• Pumping test
• Confined aquifer
• Estimation of aquifer parameters
• Well function
• Fuzzy transformation method

20.1001.1.20086377.1398.12.40.6.2

---

References

1) Abdel-Gawad, H. A. A. A., and El-
Hadi, H. A. 2009. Parameter estimation of
pumping test data using genetic algorithm.
Thirteenth International Water Technology
Conference, IWTC 13.
2) Aramaki, T., and Matsuo, T.
1998. Evaluation model of policy scenarios for
basin-wide water resources and quality
management in the Tone River. Japan, Water
Science and Technology 38: 59-67.
3) 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 28: 541-549.
4) Delnaz, A., Rakhshandehroo, G. and
Nikoo, M.R., 2017. Assessment of GRNN
model in comparison to ANN and RBF models
for estimating confined aguifer parameters.
Hydrogeology 2: 102-117.
5) Delnaz, A., Rakhshandehroo, G. and
Nikoo, M.R., 2019. Confined Aquifer’s
Hydraulic Parameters Estimation by a
Generalized Regression Neural
Network. Iranian Journal of Science and
Technology, Transactions of Civil Engineering
1-11.
6) Delnaz, A., Rakhshandehroo, G. and
Nikoo, M.R., 2019. Optimal estimation of
unconfined aquifer parameters in uncertain
environment based on fuzzy transformation
method. Water Supply 19(2):444-450.
7) Hanss, M., and Willner, K. 2000. A
fuzzy arithmetical approach to the solution of
finite element problems with uncertain
parameters. Mechanics Research
Communications 27: 257-272.
8) Hanss, M. 2002. The transformation
method for the simulation and analysis of
systems with uncertain parameters. Fuzzy Sets
and Systems 130: 277-289.
9) Hanss, M. 2003. The extended
transformation method for the simulation and
analysis of fuzzy-parameterized models.
International Journal of Uncertainty, Fuzziness
and Knowledge-Based Systems 11:711-727.
10) Jha, M. K., Kumar, A., Nanda, G., and
Bhatt, G. 2006. Evaluation of traditional and
nontraditional optimization techniques for
determining well parameters from stepdrawdown
test data. Journal of Hydrologic
Engineering 11: 617-630.
11) Kerachian, R., Fallahnia, M.,
Bazargan-Lari, M. R., Mansoori, A., and
Sedghi, H. 2010. A fuzzy game theoretic
approach for groundwater resources
management. Application of Rubinstein
bargaining theory. Resources, Conservation and
Recycling 54: 673-68.
12) Lingireddy, S. 1998. Aquifer parameter
estimation using genetic algorithms and neural
networks. Civil Engineering Systems 15: 125-
144.
13) Lu, C., Shu, L., Chen, X., and Cheng,
C. 2011. Parameter estimation for a karst
aquifer with unknown thickness using the
genetic algorithm method. Environmental Earth
Sciences 63(4): 797-807.
14) Nasiri, F., Maqsood, I., Huang, G., and
Fuller, N. 2007. Water quality index: A fuzzy
river-pollution decision support expert system.
Journal of Water Resources Planning and
Management 133: 95-105.
15) Nikoo, M. R., Kerachian, R., Karimi,
A., and Azadnia, A A. 2013. Optimal water and
waste-load allocations in rivers using a fuzzy
transformation technique: A case study,
Environmental Monitoring and Assessment
185: 2483-2502.
16) Rajesh, M., Kashyap, D., and Hari
Prasad, K. 2010. Estimation of unconfined
aquifer parameters by genetic algorithms,
Hydrological Sciences Journal 55: 403-413.
17) Sadegh, M. and Kerachian, R. 2011.
Water resources allocation using solution
concepts of fuzzy cooperative games: Fuzzy
least core and fuzzy weak least core. Water
Resources Management 25:2543-2573.
18) Sadegh, M., Mahjouri, N., and
Kerachian, R. 2010. Optimal inter-basin water
allocation using crisp and fuzzy Shapley games.
Water Resources Management 24:2291-2310.
19) Samuel, M. P. and Jha, M. K. 2003.
Estimation of aquifer parameters from pumping
test data by genetic algorithm optimization
06 تدوین یک مدل شبیهسازی-بهینهسازی فازی به منظور تخمین بهینهی فراسنجهای آبخوان محصور
technique. Journal of Irrigation and Drainage engineering129:348-359.
20) Singh, A. P., Ghosh, S., and Sharma, P.
2007. Water quality management of a stretch of river Yamuna: An interactive fuzzy multi-objective approach. Water Resources Management 21: 515-532.
21) Zhuang, C., Zhou, Z., Zhan, H., and Wang, G. 2015. A new type curve method for estimating aquitard hydraulic parameters in a multi-layered aquifer system. Journal of hydrology 527:212-220 .