Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/1250
Title: Maximum stream temperature estimation of Degirmendere River using artificial neural network
Authors: Karaçor, Adil Gürsel
Sivri, Nüket
Uçan, Osman Nuri
Keywords: Artificial neural network
Black sea
Degirmendere river
Stream temperature
Issue Date: May-2007
Publisher: CSIR
Abstract: Stream 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.
Description: 363-366
URI: http://hdl.handle.net/123456789/1250
ISSN: 0022-4456
Appears in Collections:JSIR Vol.66(05) [May 2007]

Files in This Item:
File Description SizeFormat 
JSIR 66(5) (2007) 363-366.pdf119.1 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.