Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/1250
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dc.contributor.authorKaraçor, Adil Gürsel-
dc.contributor.authorSivri, Nüket-
dc.contributor.authorUçan, Osman Nuri-
dc.date.accessioned2008-05-06T10:40:28Z-
dc.date.available2008-05-06T10:40:28Z-
dc.date.issued2007-05-
dc.identifier.issn0022-4456-
dc.identifier.urihttp://hdl.handle.net/123456789/1250-
dc.description363-366en_US
dc.description.abstractStream temperature determines the rate of the decomposition of organic matter and the saturation concentration of dissolved oxygen. Combined with industrial waste, stream temperature becomes a crucial parameter. Therefore, estimation of maximum stream temperature is very important, especially during summertime when the high temperatures may become dangerous for the habitat of rivers. A three-layered feed forward artificial neural network was developed to predict the maximum stream temperature of Degirmendere River for the five days ahead. Satisfactory results were achieved as the average prediction error turned out to be less than 1°C.en_US
dc.language.isoen_USen_US
dc.publisherCSIRen_US
dc.sourceJSIR Vol.66(5) [May 2007]en_US
dc.subjectArtificial neural networken_US
dc.subjectBlack seaen_US
dc.subjectDegirmendere riveren_US
dc.subjectStream temperatureen_US
dc.titleMaximum stream temperature estimation of Degirmendere River using artificial neural networken_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.66(05) [May 2007]

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