THE FLOW-RATE PREDICTION IN ERGENE WATERSHED

Bayrak G., Sevgen S., Samli R.

CARPATHIAN JOURNAL OF EARTH AND ENVIRONMENTAL SCIENCES, cilt.16, sa.2, ss.293-303, 2021 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.26471/cjees/2021/016/175
  • Dergi Adı: CARPATHIAN JOURNAL OF EARTH AND ENVIRONMENTAL SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Geobase
  • Sayfa Sayıları: ss.293-303
  • Anahtar Kelimeler: artificial neural networks, Ergene watershed, flow-rate prediction, multiple linear regression, support vector machine, ARTIFICIAL NEURAL-NETWORK, SUPPORT VECTOR REGRESSION, SHORT-TERM, RIVER-BASIN, MODEL, ANN, MANAGEMENT
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

This paper presents an experimental study about prediction of the highest monthly average flow-rate of the Ergene River. Hydro-meteorological data from Luleburgaz Meteorology Station (MS) and Luleburgaz Flow Observation Station (FOS) have been used for prediction. Ergene watershed has point and non-point sources pollution and has seasonal floods. The study area is located in the middle of the watershed. First of all, hydro-meteorological data of all months between 1995 and 2017 were obtained from Luleburgaz FOS. After that, the relationship between the data were modeled by Artificial Neural Network (ANN), Multiple Linear Regression (MLR) and Support Vector Machine (SVM). Also, the monthly flow-rate of Ergene River Luleburgaz Station is predicted annually for the years 2017 and 2018. The results demonstrate that the ANN, MLR and SVM models can predict the flow-rate with high accuracy, but the ANN is the most appropriate model to the Ergene watershed data set.