Ability evaluation of hybrid SOM-FL model for hydraulic conductivity estimating in Tabriz city subway area

Document Type : Research Article


M.Sc Student, Faculty of Natural Science, University of Tabriz, Iran


Increasing development of engineering projects construction such as city subway needs appropriate investigation, management and control of groundwater. Therefore, precise estimation of hydrogeological parameters such as hydraulic conductivity is the most important factor in studies and modeling of groundwater and geotechnical issues. In recent decades, various laboratory and field methods exist for estimating this parameter, but estimation of hydraulic conductivity using these methods due to the heterogeneity and anisotropy hydrogeological environments is costly, time-consuming and inherent uncertain. In this study, three fuzzy inference methods, Sugeno (SFIS), Mamdani fuzzy inference model (MFIS) and Larsen Fuzzy Inference System (LFIS) that is suitable for handling the uncertain data, was adopted to estimate the hydraulic conductivity in Tabriz city subway area. After, the hybrid SOM-FL model was presented to improve efficiency of individual model and solve heterogeneity problem of the Tabriz city aquifer. Based the evaluation criterions R2 and RMSE the results of individual models are acceptable but the proposed hybrid model improved, R2 in train and test stage, 18% and 15.4 percent, respectively.


Main Subjects

[1].Bardossy A, Duckstein L. Fuzzy rule-based modeling with applications to geophysical, biological and engineering systems. 256. CRC Press, Florida, USA:1993.
[2]. Helmy T, Fatai A, Faisal K. Hybrid computational models for the characterization of oil and gas reservoirs. Expert Syst. Appl. 2010;37(7):5353–5363.
[3].Anifowose F, Abdulraheem A. Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization. J. Nat. Gas Sci. Eng. 2011; 3(3):505–517.
[4].Nadiri AA, Chitsazan N, Tsai F.T.C, Asghari Moghaddam AA. Bayesian Artificial Intelligence Model Averaging for Hydraulic ConductivityEstimation. J. Hydrol. Eng. 2014; 19(3):520–532.
[5].Nadiri AA, Fijani E, Tsai F.T.C, Asghari Moghaddam A.A. Supervised Committee Machine with Artificial Intelligence for Prediction of Fluoride Concentration. Hydroinformatics Journal. 2013; 15(4):1474-1490.
[6]. Zadeh LA. Fuzzy sets, Information and Control. 1965;8(3):338–353.
[7].Tayfur G, Nadiri AA, Moghaddam AA. Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation. Water Resources Management. 2014; 28: 1173-1184.
[8].Vernieuwe H, Verhoest NEC, De Baets B, Hoeben R, De Troch FP. Cluster-based fuzzy models for groundwater flow in the unsaturated zone. Advances in water Resources. 2007;30(4):701-714.‏
[9].Kumar NV, Mathew S, Swaminathan G. Multifactorial Fuzzy Approach for the Assessment of Groundwater Quality. Journal of Water Resource and Protection. 2010; 2:597-608.
[10].Kisi O. Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration. Journal of Hydrology. 2013;(504):160-170.‏
[11].Srinivas R. Bhakar P, Singh AP. Groundwater quality assessment in some selected area of Rajasthan, India using fuzzy multi-criteria decision making tool. Aquatic Procedia. 2015;4:1023-1030.‏
[12].Habibi M, Nadiri A, Asghari Moghaddam A, Naderi K. 2016. Combination of geostatistical and artificial intelligence methods for predicting spatiotemporal water level in the Hadishahr plain, Iran Watershed Management Science and Engineering. 2016;(32):27-32 (Persian).
[13].Nourani V, Parhizkar M. Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall–runoff modeling. Journal of Hydroinformatics. 2013;15(3):829-848.‏
[14].Kohonen T. Self-organized formation of topologically correct feature maps. Biological cybernetics. 1982;43(1):59-69.‏
[15].Chen L, Lin D. Application of Integrated Back-Propagation Network and Self-Organizing Map for Groundwater Level Forecasting. J. Whater resour. Plann Manage. 2011;137:352-365.
[16].Nourani V, Alami MT, Vousoughi FD. Hybrid of SOM-Clustering Method and Wavelet-ANFIS Approach to Model and Infill Missing Groundwater Level Data. Journal of Hydrologic Engineering, 2016;05016018.
[17].Kohonen T, Kaski S, Lappalainen H. Self-organized formation of various invariant-feature filters in the adaptive-subspace SOM. Neural computation. 1997;9(6):1321-1344.‏
[18].Sugeno M. Industrial applications of fuzzy control. Elsevier Science Inc. 1985
[19].Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Machine Stud.1975;7(1):1–13.
[20].Larsen PM. Industrial applications of fuzzy logic control, International Journal of Man-Machine Studies. 1980; 12: 3–10.
[21].Nadiri AA. Estimate groundwater levels in the Tabriz city metro area by using artificial neural networks. Master's thesis, Department of Geology, Faculty of Natural Sciences, University of Tabriz. 2007. (Persian).
[22].Chiu S. L. Fuzzy model identification based on cluster estimation. Journal of Intelligent & fuzzy systems. 1994;2(3):267-278.‏