Maximum stream temperature estimation of Degirmendere River using artificial neural network

KARACOR A. G., Sivri N., UÇAN O. N.

JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, cilt.66, sa.5, ss.363-366, 2007 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66 Sayı: 5
  • Basım Tarihi: 2007
  • Dergi Adı: JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.363-366
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Hayır

Özet

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 degrees C.

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