Adaptive Neuro-Fuzzy Inference Technique for Estimation of Light Penetration in Reservoirs

dc.authorscopusid 7004369411
dc.authorscopusid 6602782136
dc.authorscopusid 18838670000
dc.authorscopusid 18835473500
dc.authorwosid Soyupak, Selçuk/A-9965-2008
dc.authorwosid KARAER, FEZA/AAH-3984-2021
dc.contributor.author Soyupak, Selcuk
dc.contributor.author Karaer, Feza
dc.contributor.author Senturk, Engin
dc.contributor.author Hekim, Huseyin
dc.date.accessioned 2024-07-05T14:33:09Z
dc.date.available 2024-07-05T14:33:09Z
dc.date.issued 2007
dc.department Atılım University en_US
dc.department-temp Atilim Univ, Fac Engn, Dept Civil Engn, TR-06836 Ankara, Turkey; Uludag Univ, Fac Engn & Architecture, Dept Environm Engn, Bursa, Turkey; State Hydraul Works Turkey, Bursa, Turkey en_US
dc.description.abstract An adaptive neuro-fuzzy inference technique has been adopted to estimate light levels in a reservoir. The data were collected randomly from Doganci Dam Reservoir over a number of years. The input data set is a matrix with vectors of time, depth, sampling location, and incident solar radiation. The output data set is a vector representing light measured at various depths. Randomization and logarithmic transformations have been applied as preprocessing. One-half of the data have been utilized for training; testing and validation steps utilized one-fourth each. An adaptive neuro-fuzzy inference system (ANFIS) has been built as a prediction model for light penetration. Very high correlation values between predictions and real values on light measurements with relatively low root mean square error values have been obtained for training, test, and validation data sets. Elimination of the overtraining problem was ensured by satisfying close root mean square error values for all sets. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1007/s10201-007-0204-6
dc.identifier.endpage 112 en_US
dc.identifier.issn 1439-8621
dc.identifier.issn 1439-863X
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-34547943291
dc.identifier.scopusquality Q2
dc.identifier.startpage 103 en_US
dc.identifier.uri https://doi.org/10.1007/s10201-007-0204-6
dc.identifier.uri https://hdl.handle.net/20.500.14411/893
dc.identifier.volume 8 en_US
dc.identifier.wos WOS:000248820400003
dc.identifier.wosquality Q3
dc.language.iso en en_US
dc.publisher Springer Japan Kk en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 1
dc.subject reservoirs en_US
dc.subject modeling en_US
dc.subject light penetration en_US
dc.subject neuro-fuzzy inference en_US
dc.subject ANFIS en_US
dc.title Adaptive Neuro-Fuzzy Inference Technique for Estimation of Light Penetration in Reservoirs en_US
dc.type Article en_US
dc.wos.citedbyCount 0
dspace.entity.type Publication

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