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Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models

Abstract

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results “conditioned” by nitrate-N values to raise their correlation to higher than 0.9.

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References

  • Aller L, Bennett T, Lehr JH, Petty RJ, Hackett G (1987) DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeologic settings. EPA 600/2–87-035. U.S. Environmental Protection Agency, Ada, Oklahoma

    Google Scholar 

  • Anane M, Abidi B, Lachaal F, Limam A, Jellali S (2013) GIS-based DRASTIC, pesticide DRASTIC and the susceptibility index (SI): comparative study for evaluation of pollution potential in the Nabeul-Hammamet shallow aquifer, Tunisia. Hydrogeol J 21(3):715–731

    CAS  Article  Google Scholar 

  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural network in hydrology. I: preliminary concepts. J Hydrol Eng 5:2(115):115–123. doi:10.1061/(ASCE)1084–0699(2000)

    Article  Google Scholar 

  • Asadi S, Hassan M, Nadiri A, Heather D (2014) Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. Environ Sci Pollut Res (2014) 21:8847. doi:10.1007/s11356-014-2821-z

    CAS  Article  Google Scholar 

  • Baghapour MA, Nobandegani AF, Talebbeydokhti N, Bagherzadeh S, Nadiri AA, Gharekhani M, Chitsazan N (2016) Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz plain, Iran. J Environ Health Sci Eng 2016 14:13

    Article  Google Scholar 

  • Bai LP, Wang YY, Meng FS (2012) Application of DRASTIC and extension theory in the groundwater vulnerability evaluation. J. Water Environ 26:381–391

    Article  Google Scholar 

  • Bárdossy A, Disse M (1993) Fuzzy rule-based models for infiltration. Water Resour Res 29(2):373–382

    Article  Google Scholar 

  • Bezdec JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Book  Google Scholar 

  • Bezdek KT, Hathaway R (1988) Optimally test for fixed points of the fuzzy c-mean algorithm. Pattern Recogn 21:651–663

    Article  Google Scholar 

  • Chen CH, Lin ZS (2006) A committee machine with empirical formulas for permeability prediction. J. Comput Geosci 32:485–496

    Article  Google Scholar 

  • Chen MS, Wang SW (1999) Fuzzy clustering analysis for optimizing fuzzy membership functions. Fuzzy Sets Syst 103(2):239–254

    Article  Google Scholar 

  • Chitsazan N, Nadiri AA, Tsai F, Moghaddam, A (2014) Bayesian artificial intelligence model averaging for hydraulic conductivity estimation J. Hydrol. Eng., 10.1061/(ASCE) HE.1943–5584.0000824, 520–532.

  • Chiu S (1994) Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2:267–278

    Google Scholar 

  • de Martonne E (1925) Trait’e de G’eographie Physique: 3 tomes, Paris

  • Dixon B (2004) Prediction of groundwater vulnerability using integrated GIS-based neuro-fuzzy techniques. J Spatial Hydrology 4(2):1–38

    Google Scholar 

  • Dixon B (2005) Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool. Appl Geogr 25:327–347

    Article  Google Scholar 

  • Emberger L (1930) Sur une formule applicable en g’eographie botanique. Cah Herb Seanc Acad Sci 191:389–390

    Google Scholar 

  • Fijani E, Nadiri AA, Asghari Moghaddam A, Tsai F, Dixon B (2013) Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer Iran. J Hydrol l530:89–100

    Article  Google Scholar 

  • Gemitzi A, Petalas C, Tsihrintzis VA, Pisinaras V (2006) Assessment of groundwater vulnerability to pollution: a combination of GIS, fuzzy logic and decision making techniques. Environ Geol 49:653–673

    CAS  Article  Google Scholar 

  • Grande J, Andújar J, Aroba J, Beltrán R, de la Torre M, Cerón J, Gómez T (2010) Fuzzy modeling of the spatial evolution of the chemistry in the Tinto River (SW Spain). Water Resour Manag 24(12):3219–3235

    Article  Google Scholar 

  • Huan H, Wang J, Teng Y (2012) Assessment and validation of groundwater vulnerability to nitrate based on a modified DRASTIC model: a case study in Jilin City of northeast China. Sci Total Environ 440:14–23

    CAS  Article  Google Scholar 

  • Javadi S, Kavehkar N, Mohammadi K, Khodadi A, Kahawita K (2011) Calibration DRASTIC using field measurements, sensitivity analysis and statistical method to assess groundwater vulnerability. Water Int 36:719–732

    Article  Google Scholar 

  • Kadkhodaie-Ilkhchi A, Rezaee MR, Rahimpour-Bonab H, Chehrazi A (2009) Petrophysical data prediction from seismic attributes using committee fuzzy interference system. Computer and Geosciences 35:2314–2330

    Article  Google Scholar 

  • Kim YJ, Hamm S (1999) Assessment of the potential for groundwater contamination using DRASTIC/EGIS technique, Cheongju area, South Korea. Hydrogeol J 7(2):227–235

    Article  Google Scholar 

  • Kord M, Asghari Moghaddam A (2013) Spatial analysis of Ardabil plain aquifer potable groundwater using fuzzy logic. J. King Saud University – Science. 1–12

  • Kord M, Asghari Moghaddam A, Nakhaeei M (2013) Investigation of hydrogeological characteristics of Ardabil plain aquifer. ISESCO JOURNAL of Science and Technology 9(15):63–69

    Google Scholar 

  • Labani MM, Kadkhodaie-Ilkhchi A, Salahshoor K (2010) Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: a case study from the Iranian part of the South Pars Gas Field. Persian Gulf Basin Journal of Petroleum Science and Engineering 72:175–185

    CAS  Article  Google Scholar 

  • Larsen PM (1980) Industrial application of fuzzy logic control. International Journal of Man-Machine Studies. 12:3–10

    Article  Google Scholar 

  • Lee KH (2004) First course on fuzzy, theory and applications. Springer, Berlin, 335p

    Google Scholar 

  • Li H, Philip CL, Huang HP (2001) Fuzzy neural intelligent systems: mathematical foundation and the applications in engineering. CRC Press, Inc., Boca Raton, FL

    Google Scholar 

  • Mamdani EH (1976) Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies. 8:669–678

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experimental in linguistic synthesis with a fuzzy logic control. International Journal of Man-Machine Studies 7:1–13

    Article  Google Scholar 

  • Misstear BDR, Brown L, Daly D (2009) A methodology for making initial estimates of groundwater recharge from groundwater vulnerability mapping. Hydrogeol J 17(2):275–285

    Article  Google Scholar 

  • Mohammadi K, Niknam R, Majd VJ (2009) Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran-Karaj aquifer. Iran Environ Geol 58:437–446

    Article  Google Scholar 

  • Nadiri AA (2015) Application of artificial intelligence methods in geosciences and hydrology. OMICS International Publications.

  • Nadiri AA, Fijani E, Tsai FT-C, Asghari Moghaddam AA (2013) Supervised committee machine with artificial intelligence for prediction of fluoride concentration. J Hydroinf 15(4):1474–1490

    CAS  Article  Google Scholar 

  • Nadiri AA, Gharekhani M, Khatibi R, Sadeghfam S, Asgari Moghaddam A (2017) Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM). Sci Total Environ 574:691–706 (In press)

    CAS  Article  Google Scholar 

  • Nadiri AA, Marwa H, Asadi S (2015) Supervised intelligence committee machine to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. Transportation Research Record: Journal of the Transportation Research Board 2528:96–105

    Article  Google Scholar 

  • Naftaly U, Intrator N, Horn D (1997) Optimal ensemble averaging of neural networks. Comput Neural Syst 8:283–296

    Article  Google Scholar 

  • Neshat A, Pardhan B (2014) An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment. Nat Hazards. doi:10.1007/s11069-014-1503-y

    Google Scholar 

  • Newton SC, Pemmaraju S, Mitra S (1992) Adaptive fuzzy leader clustering of complex data sets in pattern recognition. IEEE Transactions on Neural Networks 5:794–800

    Article  Google Scholar 

  • Nourani V, Asgharimoghaddam A, Nadiri AA (2008b) Forecasting spatiotemporal water levels of Tabriz aquifer. Trends in Applied Sciences Research 3(4):319–329

    Article  Google Scholar 

  • Nourani V, Mogaddam AA, Nadiri AA (2008a) An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol Process 22(26):5054–5066

    Article  Google Scholar 

  • Panagopoulos GP, Antonakos AK, Lambrakis NJ (2006) Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical method and GIS. Hydrogeol J 14(6):894–911

    CAS  Article  Google Scholar 

  • Rahimzadeh F, Babakhani AR (1987) Geological map of Ardabil (1:250,000). Geological Survey of Iran

  • Rezaei F, Safavi HR, Ahmadi A (2013) Groundwater vulnerability assessment using fuzzy logic: a case study in the Zayandehrood aquifers, Iran. J Environmental Management 51:267–277

    Google Scholar 

  • Sadeghfam S, Hassanzadeh Y, Nadiri AA, Zarghami M (2016) Localization of groundwater vulnerability assessment using catastrophe theory. Water Resour Manag 30(13):4585–4601

    Article  Google Scholar 

  • Scanlon B, Healy R, Cook P (2002) Choosing appropriate techniques for quantifying groundwater recharge. Hydrology Journal 10(1):18–39

    CAS  Google Scholar 

  • Sener E, Davraz A (2013) Assessment of groundwater vulnerability based on a modified DRASTIC model, GIS and an analytic hierarchy process (AHP) method: the case of Egirdir Lake basin (Isparta, Turkey). J Hydrogeology 21:701–714

    Article  Google Scholar 

  • Sener E, Sener S, Davraz A (2009) Assessment of aquifer vulnerability based on GIS and DRASTIC methods: a case study of the Senirkent-Uluborlu Basin (Isparta, Turkey). Hydrogeol J 17:2023

    CAS  Article  Google Scholar 

  • Şener E, Şener Ş (2015) Evaluation of groundwater vulnerability to pollution using fuzzy analytic hierarchy process method. Environmental Earth Sciences 73:8405–8424

    Article  Google Scholar 

  • Su XS, Xu W, Du SH (2014a) Responses of groundwater vulnerability to artificial recharge under extreme weather conditions in Shijiazhuang City. China J Water Suppl: Res Technol–Aqua 63:224–238

    Article  Google Scholar 

  • Sugeno M (1985) Industrial application of fuzzy control. North-Holland, New York

    Google Scholar 

  • Tayfur G, Nadiri A, Moghaddam A (2014) Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resour Manag 28(4):1173–1184

    Article  Google Scholar 

  • USEPA (2009) National Primary Drinking Water Regulations. US Environmental Protection Agency. EPA816-F-09-004

  • WHO (2009). Guidelines for drinking-water quality. Word Health Organization.

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge gratefully the provision of data by the Ardabil Regional Water Authority.

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Correspondence to Ata Allah Nadiri.

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Responsible editor: Marcus Schulz

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Nadiri, A.A., Gharekhani, M., Khatibi, R. et al. Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Environ Sci Pollut Res 24, 8562–8577 (2017). https://doi.org/10.1007/s11356-017-8489-4

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  • DOI: https://doi.org/10.1007/s11356-017-8489-4

Keywords

  • Ardabil aquifer
  • Fuzzy logic
  • Supervised committee fuzzy logic (SCFL)
  • Vulnerability index