nach oben

Neural Computing and Applications

Erschienen in:

01.12.2013 | Original Article

Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches

verfasst von: Mustafa Demirci, Ahmet Baltaci

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Prediction of sediment concentration in a river is very important for many water resource projects. Conventional sediment rating curves (SRC), however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. A comparison was performed between fuzzy logic (FL), SRC and multilinear regression models. It was based on a 5-year period of continuous streamflow, suspended sediment concentration and mean water temperature data of Sacremento Freeport Station operated by the United States Geological Survey. Based on the comparison of the results, it is found that the FL model gives better estimates than the other techniques.

Vorheriger Artikel A fast fuzzy support vector machine based on information granulation

Nächster Artikel Incorporating the effects of hike in energy prices into energy consumption forecasting: a fuzzy expert system

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

McBean EA, Al-Nassri S (1988) Uncertainty in suspended sediment transport curves. Journal of Hydraulic Engineering, ASCE 114(1):63–74CrossRef

Kisi O (2003) Modelling of suspended sediment yield in a river cross-section using fuzzy logic. PhD thesis, Istanbul Technical University Institute of Science and Technology, Istanbul, Turkey

Kisi O (2004) Daily suspended sediment modelling using a fuzzy differential evolution approach. Hydrol Sci J 49(1):183–197CrossRef

Kisi O (2005) Daily river flow forecasting using artificial neural networks and autoregressive models. Turk J Eng Environ Sci 29:9–20

Tayfur G, Ozdemir S, Singh VP (2003) Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Adv Water Res 26(12):1249–1256CrossRef

Kisi O, Karahan ME, Sen Z (2006) River suspended sediment modeling using fuzzy logic approach. Hydrol Process 20(20):4351–4362CrossRef

Lohani AK, Goel NK, Bhatia KKS (2007) Deriving stage—discharge–sediment concentration relationships using fuzzy logic. Hydrol Sci J 52(4):793–807CrossRef

Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367:52–61CrossRef

Lopes VL, Ffolliott PF (1993) Sediment rating curves for a clearcut ponderosa pine watershed in northern Arizona. Water Resour Bull 29(3):369–382CrossRef

10.

Russel SO, Campbell PF (1996) Reservoir operating rules with fuzzy programming. J Water Res Planning Manage ASCE 122(3):165–170CrossRef

11.

Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):38–53MathSciNetCrossRef

12.

Klir GJ, Folger TA (1988) Fuzzy sets, uncertainty and information. Prentice Hall, New YorkMATH

13.

Zadeh L, Kacprzyk J (1992) Fuzzy logic for the management of uncertainty. Wiley, New York

14.

Kosko B (1992) Neural networks and fuzzy systems. Prentice Hall, Englewood CliffsMATH

15.

Kosko B (1993) Fuzzy thinking: the new science of fuzzy logic. Hyperion, New York

16.

Ross TJ (1995) Fuzzy logic with engineering applications. McGraw-Hill, New York

17.

Piest RF, Miller CR (1975) Sediment yields and sediment sources. In: Vanoni VA (ed) Sedimentation engineering. ASCE, New York

18.

Campbell FB, Bauder HA (1940) A rating-curve method for determining silt discharge of streams. EOS Trans Am Geophys Union 21:603–607CrossRef

19.

Bayazıt M, Oguz B (1998) Probability and statistics for engineers. Birsen Publishing House, Istanbul, p 159

Titel: Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches
verfasst von: Mustafa Demirci
Ahmet Baltaci
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1280-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Sonderheft 1/2013

Developing a new model based on neuro-fuzzy system for predicting roof fall in coal mines

A knowledge-based Mamdani fuzzy logic prediction of the motion resistance coefficient in a soil bin facility for clay loam soil

Genetic algorithm-based adaptive fuzzy sliding mode controller for electronic throttle valve

Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors

A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction

Superior neuro-fuzzy classification systems

Premium Partner