1. A. Chowdhury, M.K. Jha, V.M. Chowdary, B.C. Mal, Integrated remote sensing and GIS based approach for assessing groundwater potential in West Medinipur district, West Bengal, India, Int. J. Remote Sens., 30 (2009) 231–250.
  2. D. Oikonomidis, S. Dimogianni, N. Kazakis, K. Voudouris, A GIS/remote sensing-based methodology for groundwater potentiality assessment in Tirnavos area, Greece, J. Hydrol., 525 (2015) 197–208.
  3. N. Thilagavathi, T. Subramani, M. Suresh, D. Karunanidhi, Mapping of ground water potential zones in Salem Chalk Hills, Tamil Nadu, India using remote sensing and GIS techniques, Environ. Monit. Assess., 187 (2015) 164.
  4. A.Y. Murat, Ozgur Kisi, Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques, KSCE J. Civil Eng., 21 (2017) 1631–1639.
  5. E. Yel, S. Yalpir, Prediction of primary treatment effluent parameters by fuzzy inference system (FIS) approach, Procedia Comput. Sci., 3 (2011) 659–665.
  6. H.N.H. Zen, L.W. Trimartanti, Z. Abidin, A.M. Abadi, Determining hydrocarbon prospective zone using the combination of qualitative analysis and fuzzy logic method, J. Syst. Sci. Syst. Eng., 26 (2017) 463–474.
  7. Zh. Muka, E. Cenaj, R. Dervis, Modeling the amount of rainfall using fuzzy logic, Int. J. Innov. Sci. Eng. Technol., 4 (2017) 207–210.
  8. A. Akgun, E.A. Sezer, H.A. Nefeslioglu, C. Gokceoglu, B. Pradhan, An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm, Comput. Geosci., 38 (2012) 23–34.
  9. A. Porwal, R.D. Das, B. Chaudhary, I. Gonzalez-Alvarez, O. Kreuzer, Fuzzy inference systems for prospectivity modeling of mineral systems and a case-study for prospectivity mapping of surficial Uranium in Yeelirrie Area, Western Australia, Ore Geol. Rev., 71 (2015) 839–852.
  10. N. Alavi, V. Nozari, S.M. Mazloumzadeh, H. Nezamabadipour, Irrigation water quality evaluation using adaptive networkbased fuzzy inference system, Paddy Water Environ., 8 (2010) 259–266.
  11. R. Mirabbasi, S.M. Mazloumzadeh, M.B. Rahnama, Evaluation of irrigation water quality using fuzzy logic, Res. J. Environ. Sci., 2 (2008) 340–352.
  12. E. Cox, Fuzzy Fundamentals, IEEE Spect., 29 (1992) 58–61.
  13. T.J. Ross, Fuzzy Logic with Engineering Applications, John Wiley and Sons, 2010.
  14. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man Cybern., 15 (1985) 116–132.
  15. A. Zaher, Y. Ngoran, F. Thiery, S. Grieu, A. Traore, Fuzzy rule-based model for optimum orientation of solar panels using satellite image processing, J. Phys. Conf., 783 (2017) 1–11.
  16. K. Kapil, Shikhar Deep, G.K. Surindra Suthar, M.G. Dastidar, T.R. Sreekrishnan, Application of fuzzy inference system (FIS) coupled with Mamdani’s method in modelling and optimization of process parameters for biotreatment of real textile wastewater, Desal. Wat. Treat., 57 (2015) 9690–9697.
  17. H.R. Pourghasemi, M. Beheshtirad, B. Pradhan, A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping, Geomat. Nat. Hazards Risk, 7 (2016) 861–885.
  18. A. Beycioglu, A. Gultekin, H.Y. Aruntas, O. Gencel, M. Dobiszewska, W. Brostow, Mechanical properties of blended cements at elevated temperatures predicted using a fuzzy logic model, Comput. Concr., 20 (2017) 247–255.
  19. H. Naderpour, S.A. Alavi, Application of Fuzzy Logic in Reinforced Concrete Structures, Proc. 4th International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Civil-Comp Press, 2015.
  20. A. Mojtaba, Optimized Mamdani fuzzy models for predicting the strength of intact rocks and anisotropic rock masses, J. Rock Mech. Geotech. Eng., 8 (2016) 218–224.
  21. C. Gokceoglua, K. Zorlu, A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock, Eng. Appl. Art. Int., 17 (2009) 61–72.
  22. K. Karimpour, R. Zarghami, M.A. Moosavian, H. Bahmanyar, New fuzzy model for risk assessment based on different types of consequences, Oil Gas Sci. Technol., 71 (2016) 1–15.
  23. P. Mahalakshmi, K. Ganesan, Mamdani fuzzy rule based model to classify sites for aquaculture development, Indian J. Fish., 62 (2015) 110–115.
  24. G.E. Meyer, Digital camera operation and fuzzy logic classification of uniform plant, soil, and residue color images, Appl. Eng. Agric., 20 (2004) 519–529.
  25. K. Chao, Y. Chen, R.H. Early, B. Park, Color image classification systems for poultry viscera inspection, Appl. Eng. Agric., 15 (1999) 363–369.
  26. V. Kansal, A. Kaur, Comparison of Mamdani-type and Sugeno type FIS for water flow rate control in a Rawmill, Int. J. Sci. Eng. Res., 4 (2013) 2580–2584.
  27. N. Tremblay, M.Y. Bouroubi, B. Panneton, S. Guillaume, P. Vigneault, C. Belec, Fuzzy logic to combine soil and crop growth information for estimating optimum N rate for corn, EFITA Conference, Vol. 9, 2009, pp. 397–404.
  28. A. Xing Zhu, Feng Qi, Amanda Moore, James. E. Burt, Prediction of soil properties using membership values, Geoderma, 158 (2010) 199–206.
  29. N. Duru, F. Dokmen, M.M. Canbay, C. Kurtulus, Soil productivity analysis based on a fuzzy logic system, J. Sci. Food Agric., 90 (2010) 2220–2227.
  30. E. Ilbahar, A. Karaşan, S. Cebi, C. Kahraman, A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system, Safety Sci., 103 (2018) 124–136.
  31. M. Blej, M. Azizi, Comparison of Mamdani-type and Sugenotype fuzzy inference systems for fuzzy real time scheduling, Int. J. App. Eng. Res., 11 (2016) 11071–11075.
  32. M. Kevin, Passino, S. Yurkovich, Fuzzy Control, Addison Wesley Longman, C.A. Menlo Park, 1998.
  33. L.A. Zadeh, Fuzzy sets, information and control, J. Symbolic Logic, 8 (1965) 338–353.
  34. L. Zhang, B. Zhang, The structure analysis of fuzzy sets, Int. J. Approx. Reason., 40 (2005) 92–108.
  35. J. Wang, D. Ding, O. Liu, M. Li, A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems, Appl. Soft Comput., 39 (2016) 11–20.
  36. A.D. Sheena, M. Ramalingam, B. Anuradha, A Comprehensive study on fuzzy inference system and its application in the field of engineering, Int. J. Eng. Trends Tech., 54 (2017) 36–40.
  37. E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man Mach. Stud., 7 (1975) 1–13.