Document Type : Original Research Paper

Authors

1 M.Sc. Student, Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

2 Associated Professor, Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

Abstract

Today Ground water is the main source of drinking, agriculture and other uses for humans. The demand for this critical and strategic natural resource increased with population growth and development of society. This increasing has been declining water resources and damage aquifers environment. Therefore, we need to manage aquifers and understanding the hydrogeological parameters to deal with the water crisis and prevent the distraction of aquifers. The one of most important parameter is hydraulic conductivity. Although, the ground water system is a complex system and estimation of hydrogeological parameters is associated with inherent uncertainty and also is costly and time consuming that usually done with classical methods such as laboratory tests, slug test, tracing test and pumping tests. So recently use artificial intelligence methods for estimation of hydraulic conductivity, reduced the uncertainty of this parameter and it adds up some accuracy. So that it can overcome on the shortcoming of classical methods. In this study, four artificial intelligence methods; mamdani fuzzy logic(MFL) system, sugeno fuzzy logic(SFL) system, Wavelet-neural network method and Least square support vector machine(LS-SVM) method were used as individual models to estimate the hydraulic conductivity by using of surface geophysical data in Maragheh-Bonab aquifer. Given that each these models based on their inherent properties, they presented good results in some parts of area. Therefore, for concurrent use of performance of all these models the nonlinear combination method as a supervised committee machine artificial intelligence (SCMAI) model were used to estimate the hydraulic conductivity in maragheh-bonab aquifer. The result of this model showed that this new combinational model has high performance than other single models that presented by using different evaluation criteria. Therefore, this model could also be used for estimation hydrogeological parameters in areas with high complexity. The SCMAI model was tested against 15 data. The RMSE and  for SCMAI prediction were computed as 0.045 and 0.97, respectively. Comparing the error measure values with dose of individual models above, it is seen that SCMAI outperforms individual AI models with low RMSE and high  values. This result implies that SCMAI model shows high performance for estimation the hydraulic conductivity values in the heterogeneous unconfined aquifer in Maragheh-Boanb plain.

Keywords

References
Alyamani, M. and Sen, Z., 1993- Determination of hydraulic conductivity from complete grain size distribution curves. Ground Water, 31, 551-555.
Anifowose, F. and Abdulraheem, A., 2011- Fuzzy logic-driven and SVM-driven hybrid computational intelligence  models applied to oil and gas reservoir characterization. J Nat Gas Sci Eng, 3(3):505–517. 
Asadi, S., Hassan, M., Nadiri, A. A. and Dylla, H., 2014- Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. Environmental Science and Pollution Research, 21 (14): 8847-8857
Bárdossy, A. and Disse, M., 1993- Fuzzy rule-based models for infiltration. Water Resour Res, 29(2):373–382.
Bouwer, H., 1989- The Bouwer and Rice slug test- An update. Ground Water, 27: 304-309.
Carman, P. C., 1956- Flow of Gases Through Porous Media. Butterworths, London, Great Britain, 182 p.
Chitsazan, N., Nadiri, A. A. and Tsai, F. F. C., 2015- Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging, Journal of Hydrology, 528: 52-62.
Chow, V. T., 1952- On the determination of transmissibility and storage coefficnts from pumping test data. Transactions, American Geophysical Union, 33, 397-404.
Chu, H. J. and Chang, L.C., 2009- Application of optimal control and fuzzy theory for dynamic groundwater remediation design. Water Resour Manag, 23(4):647–660. 
Colin, F., Guillaume, S. and Tisseyre, B., 2011- Small catchment agricultural management using decision variables defined at catchment scale and a fuzzy rule-based system: a Mediterranean vineyard case study. Water Resour Manag 25(11):2649–2668. 
Cooper, H. H. and Jacob, C. E., 1946- A generalized graphical method for evaluating formation constants and summarizing well field history. Trans. Amer. Geophysical union,  27: 526-534.
Cooper, H. H., Bredehoeft, J. D. and Papadopulos, I. S., 1967- Response of a finite diameter well to an instantaneous charge of water. Water Resource Research, 3: 263-269.
Dhar, A. and Patil, R. S., 2012- Multiobjective design of groundwater monitoring network under epistemic uncertainty.Water Resour Manag, 26(7):1809–1825.
Fair, G. M. and Hatch, L. P., 1933- Fundamental factors governing the stream line flow of water through sand. Journal of American Water Work Association.. 25: 1551-1565.
Garcia, L. A. and Shigidi, A., 2006- Using neural networks for parameter estimation in groundwater. J Hydrol 318(1–4): 215–231.
Gaur, S., Sudheer, Ch., Graillot, D., Chahar, B. R., and Kumar, D. N., 2013- Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resour Manag 27(3):927–941.
Hazen, A., 1892- Some physical properties of sands and gravels. Massachusetts state board of health 24th Annual Report, 539-556.
Helmy, T., Fatai, A. and Faisal, K., 2010- Hybrid computational models for the characterization of oil and gas reservoirs. Expert Syst Appl, 37(7):5353–5363.
Huang, Y, Gedeon, T. D. and Wong, P. M., 2010- An integrated neural-fuzzy-genetic-algorithm using hyper-surface membership functions to predict permeability in petroleum reservoirs. Eng Appl Artif Intell 14(1):15–21.
Hurtado, N., Aldana, M. and Torres, J., 2009- Comparison between neuro-fuzzy and fractal models for permeability prediction. Comput Geosci, 13:181–186.
Hvorslev, M. G., 1951- Time lag and soil pearmeability in groundwater observations. Bulletin No. 36, US Army Corps of Engineering, Waterways Experiments Stations, Vicksburg, Mississippi. 49pp.Isaaks EH and Srivastava RM, 1989. Applied Geostatistics. Oxford Universisity press, London. 561p.
Inan, T. and Tayfur, G., 2012- A prediction model for the level of well water. Sci Res Essays 7(50):4242–4252.
Malki, H. A. and Baldwin, J., 2002- A neuro-fuzzy based oil/gas producibility estimation method. IEEE Int Jt Conf Neural Netw, 1:896–901.
Mamdani, E. H. and Assilian, S., 1975- An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7: 1-13.
Merdun, H., Inar, O. C., Meral, R., Apan, M., 2006- Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity, Soil Tillage Res 90:108–116.
Mohanty, S., Jha, M. K., Kumar, A. and Sudheer, K. P., 2010- Artificial neural network modeling for groundwater level forecasting in a River Island of Eastern India. Water Resour Manag, 24(9):1845–1865. 
Moosavi, V., Vafakhah, M., Shirmohammadi, B., Behnia, N., 2013- AWavelet-ANFIS hybrid model for groundwaterlevel forecasting for different prediction periods. Water Resour Manag 27(5):1301–1321. 
Morankar, D. V., Raju, K. S. and Kumar, D. N., 2013- Integrated sustainable irrigation planning with multiobjective fuzzy optimization approach, Water Resour Manag, 27(11):3981–4004.
Motaghian, H. R. and Mohammadi, J., 2011- Spatial estimation of saturated hydraulic conductivity from terrain attributes using regression, kriging, and artificial neural networks. Pedosphere, 21(2):170–177.
Nadiri, A. A., 2015- Application of Artificial Intelligence methods in Geosciences and Hydrology. OMICS Publication. 124p.
Nadiri, A. A., Fijani, E., Tsai, F. T. C. and Asghari Moghaddam, A., 2013- Supervised committee machine with artificial intelligence for prediction of fluoride concentration, Journal of Hydroinformatics, 15: 1474-1490.
Nadiri, A. A., Gharekhani, M., Khatibi, R. and Moghaddam, A. A., 2017a- Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Environmental Science and Pollution Research, 24 (9): 8562-8577.
Nadiri, A. A., Gharekhani, M., Khatibi, R., Sadeghfam, S. and Moghaddam, A. A., 2017b- Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM).Science of the Total Environment, 574: 691-706.
Nadiri, A. A., Hassan, M. M. and Asadi, S., 2015- Supervised intelligence committee machine to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. Transportation Research Record: Transportation Research Record: Journal of the Transportation Research Board. 2528, 96-105.
Nadiri, A. A., Sedghi, Z., Khatibi, R. andGharekhani, M., 2017c- Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures. Science of The Total Environment, 593: 75-90
Nadiri, A., Chitsazan, N., Tsai, F. and Moghaddam, A., 2014- Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation, J. Hydrol. Eng., 10.1061/(ASCE)HE.1943-5584.0000824, 520-532.
Nayak, P. C., Rao, Y. R. S. and Sudheer, K. P., 2006- Groundwater level forecasting in a shallow aquifer using artificial neural network approach, Water Resour Manag, 20(1):77–90. (http://link.springer.com/article/10.1007/s11269-006-4007-z).
Neuman, S. P., 1975- Analysis of pumping test data from anisotropic unconfined aquifers considering delayed gravity response, water resources research, 11: 329-342.
Nourani, V., Hosseini Baghanam, A., Adamowski, J. and Kisi, O., 2014- Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology, 514: 358-377.
Olatunji, S. O., Selamat, A. and Abdulraheem, A., 2011- Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems. Comput Ind, 62:147–163.
Ross, J., Ozbek, M. and Pinder, G. F., 2007- Hydraulic conductivity estimation via fuzzy. Math Geol 39(8):765–780.
Sadeghfam, S., Hassanzadeh, Y., Nadiri, A. A. and Zarghami, M., 2016- Localization of groundwater vulnerability assessment using catastrophe theory. Water resources management, 30(13): 4585-4601.
Safavi, H. R., Chakraei, I., Samani, A. K., Golmohammadi, M. H., 2013- Optimal reservoir operation based on conjunctive use of surface water and groundwater using neuro-fuzzy systems. Water Resour Manag, 27(12):4259–4275.
Samani, N., Gohari-Moghadam, M. and Safavi, A. A, 2007- A simple neural network model for the determination of aquifer parameters. J Hydrol, 340:1–11.
Schaap, M. G. and Leij, F. J., 1998- Using neural networks to predict soil water retention and soil hydraulic conductivity, Soil Tillage Res 47:37–42.
Sezer, A., Göktepe, A. B. and Altun, S., 2010- Adaptive neuro-fuzzy approach for sand permeability estimation. EnvironEng Manag J, 9(2):231–238.
Shepherd, R. G, 1989- Correlations of permeability and grain size. Ground Water, 27, 633-638.
Shirmohammadi, B., Vafakhah, M., Moosavi, V. and Moghaddamnia, A., 2013- Application of several data-drive  techniques for predicting groundwater level. Water Resour Manag 27(2):419–432.
Sperry, M. S. and Peirce, J. J., 1995- A model for estimating the hydraulic conductivity of granular material based on grain shape, grain size and porosity. Ground Water, 33: 892-898.
Sugeno, M., 1985- An introductory survey of fuzzy control. Inf. Sci. (NY), 36: 59–83.
Sun, J., Zhao, Z. and Zhang, Y., 2011- Determination of three dimensional hydraulic conductivities using a combined analytical/neural network model. Tunn Undergr Space Technol 26:310–319. 
Suykens, J. A. K., Van, G. T., Brabanter, J. D., De, M. B. and Vandewalle, J., 2002- Least Squares Support Vector Machines, World Scientific Publishing, Singapore.
Tamari, S., Wosten, J. H. M. and Ruiz-Suarez, J. C., 1996- Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci Soc Am J 60(6):1732–1741.
Tayfur, G., 2012- Soft computing in water resources engineering. WIT Press, Southampton.
Tayfur, G., Moramarco, T. and Singh, V. P., 2007- Predicting and forecasting flow discharge at sites receiving significant Lateral inflow. Hydrol Process, 21(14):1848–1859.
Tayfur, G., Nadiri, A. A. and Asghari Moghaddam, A., 2014- Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation. Water Resour Manage, 28:1173–1184.
Theis, C. V., 1935-  the relation between lowering of the piezometric surface and the rate and duration of discharge of the well using groundwater storage. Trans. Amer. Geop. Union, 2:519-524.
Tutmez, B, 2010- Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system. Neural Comput Appl, 19:499–505.
Tutmez, B. and Hatipoglu, Z., 2007- Spatial estimation model of porosity. Comput Geosci 33:465–475.
Zadeh, L. A., 1965- Fuzzy sets. Information and Control, 8(3): 338-353