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2014 | OriginalPaper | Buchkapitel

7. Fuzzy Models

verfasst von : Shahab Araghinejad

Erschienen in: Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering

Verlag: Springer Netherlands

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Abstract

While variables in mathematics usually take numerical values, in fuzzy logic applications, the non-numeric linguistic variables are often used to facilitate the expression of rules and facts. The idea of fuzzy logic is very suitable for engineering application where a precise representation of the real world is sought. In contrast to the statistical-based methods, fuzzy models do not need very strong assumptions and requirements. As far as the engineering application of fuzzy logic is concerned, two approaches are usually followed up: (1) developing fuzzy extensions of the classic methods and models and (2) developing models, which are basically originated by the fuzzy logic. Basic information in fuzzy logic, fuzzy clustering, fuzzy inference systems, and fuzzy regression are the main subjects which are presented in this chapter. Obviously, the related useful MATLAB commands are presented and discussed to support the methods of applied modeling of the presented subjects.

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Literatur
Zurück zum Zitat Bardossy A, Bogardi I, Duckstein L (1990) Fuzzy regression in hydrology. Water Resour Res 26(7):1497–1508CrossRef Bardossy A, Bogardi I, Duckstein L (1990) Fuzzy regression in hydrology. Water Resour Res 26(7):1497–1508CrossRef
Zurück zum Zitat Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203 Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203
Zurück zum Zitat Casper M, Gemmar P, Gronz O, Johst M, Stuber M (2007) Fuzzy logic-based rainfall-runoff modelling using soil moisture measurements to represent system state. Hydrol Sci J 52(3):478–490CrossRef Casper M, Gemmar P, Gronz O, Johst M, Stuber M (2007) Fuzzy logic-based rainfall-runoff modelling using soil moisture measurements to represent system state. Hydrol Sci J 52(3):478–490CrossRef
Zurück zum Zitat Chang F-J, Chang Y-T (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Res 29:1–10CrossRef Chang F-J, Chang Y-T (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Res 29:1–10CrossRef
Zurück zum Zitat Firat M, Turan ME, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374:235–241CrossRef Firat M, Turan ME, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374:235–241CrossRef
Zurück zum Zitat Kucukmehmetoglu M, Şen Z, Özger M (2010) Coalition possibility of riparian countries via game theory and fuzzy logic models. Water Resour Res 46:W12528CrossRef Kucukmehmetoglu M, Şen Z, Özger M (2010) Coalition possibility of riparian countries via game theory and fuzzy logic models. Water Resour Res 46:W12528CrossRef
Zurück zum Zitat Lee M, McBean EA, Ghazali M, Schuster CJ, Huang JJ (2009) Fuzzy-logic modeling of risk assessment for a small drinking-water supply system. J Water Resour Plann Manage 135(6):547–552CrossRef Lee M, McBean EA, Ghazali M, Schuster CJ, Huang JJ (2009) Fuzzy-logic modeling of risk assessment for a small drinking-water supply system. J Water Resour Plann Manage 135(6):547–552CrossRef
Zurück zum Zitat Lohani AK, Goel NK, Bhatia KKS (2007) Deriving stage–discharge–sediment concentration relationships using fuzzy logic. Hydrol Sci J 52(4):793–807CrossRef Lohani AK, Goel NK, Bhatia KKS (2007) Deriving stage–discharge–sediment concentration relationships using fuzzy logic. Hydrol Sci J 52(4):793–807CrossRef
Zurück zum Zitat Mathon BR, Ozbek MM, Pinder GF (2008) Transmissivity and storage coefficient estimation by coupling the Cooper–Jacob method and modified fuzzy least-squares regression. J Hydrol 353:267–274CrossRef Mathon BR, Ozbek MM, Pinder GF (2008) Transmissivity and storage coefficient estimation by coupling the Cooper–Jacob method and modified fuzzy least-squares regression. J Hydrol 353:267–274CrossRef
Zurück zum Zitat Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Res 32:88–97CrossRef Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Res 32:88–97CrossRef
Zurück zum Zitat Özelkan EC, Ni F, Duckstein L (1996) Relationship between monthly atmospheric circulation patterns and precipitation: fuzzy logic and regression approaches. Water Resour Res 32(7):2097–2103CrossRef Özelkan EC, Ni F, Duckstein L (1996) Relationship between monthly atmospheric circulation patterns and precipitation: fuzzy logic and regression approaches. Water Resour Res 32(7):2097–2103CrossRef
Zurück zum Zitat Ren L, Xiang X-Y, Ni J-J (2013) Forecast modeling of monthly runoff with adaptive neural fuzzy inference system and wavelet analysis. J Hydrol Eng 18(9):1133–1139CrossRef Ren L, Xiang X-Y, Ni J-J (2013) Forecast modeling of monthly runoff with adaptive neural fuzzy inference system and wavelet analysis. J Hydrol Eng 18(9):1133–1139CrossRef
Zurück zum Zitat Shrestha RR, Simonovic SP (2010) Fuzzy nonlinear regression approach to stage-discharge analyses: case study. J Hydrol Eng 15(1):49–56CrossRef Shrestha RR, Simonovic SP (2010) Fuzzy nonlinear regression approach to stage-discharge analyses: case study. J Hydrol Eng 15(1):49–56CrossRef
Zurück zum Zitat Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349:31–43CrossRef Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349:31–43CrossRef
Zurück zum Zitat Talei A, Chua LHC, Wong TSW (2010) Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling. J Hydrol 391:248–262CrossRef Talei A, Chua LHC, Wong TSW (2010) Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling. J Hydrol 391:248–262CrossRef
Zurück zum Zitat Terzi Ö, Keskin ME, Taylan ED (2006) Estimating evaporation using ANFIS. J Irrig Drain Eng 132(5):503–507CrossRef Terzi Ö, Keskin ME, Taylan ED (2006) Estimating evaporation using ANFIS. J Irrig Drain Eng 132(5):503–507CrossRef
Zurück zum Zitat Wang K-H, Altunkaynak A (2012) A comparative case study of rainfall-runoff modeling between SWMM and FUZZY logic approach. J Hydrol Eng 17(2):283–291CrossRef Wang K-H, Altunkaynak A (2012) A comparative case study of rainfall-runoff modeling between SWMM and FUZZY logic approach. J Hydrol Eng 17(2):283–291CrossRef
Metadaten
Titel
Fuzzy Models
verfasst von
Shahab Araghinejad
Copyright-Jahr
2014
Verlag
Springer Netherlands
DOI
https://doi.org/10.1007/978-94-007-7506-0_7