Application of linear stochastic models to monthly flow data of Kelkit Stream

Yurekli K., Kurunc A., Ozturk F.

ECOLOGICAL MODELLING, cilt.183, sa.1, ss.67-75, 2005 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 183 Sayı: 1
  • Basım Tarihi: 2005
  • Doi Numarası: 10.1016/j.ecolmodel.2004.08.001
  • Dergi Adı: ECOLOGICAL MODELLING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.67-75
  • Anahtar Kelimeler: Kelkit Stream, historical data, monthly flow, autoregressive integrated moving average model, simulation, MINIMUM AIC PROCEDURE, TIME-SERIES, AUTOREGRESSIVE MODEL, RELIABILITY, IDENTIFICATION, RIVERS, ARIMA
  • Gazi Üniversitesi Adresli: Hayır

Özet

This paper presents a methodology on modeling of historical data for monthly flows from Kelkit Stream. The watershed is located on the north Anatolia and the stream is formed by joining together of small streams. For the modeling purpose, linear stochastic models known as either Box-Jenkins or ARIMA (autoregressive-moving average) were used to simulate monthly data. Diagnostic checks were done for all the models selected from the autocorrelation function (ACF) and partial autocorrelation function (PACF). The models that have the minimum Schwarz Bayesian Criterion (SBC) among the selected models fulfilled all the diagnostic checks were assumed as the best model for monthly data. For five years, the predicted data using the best models is compared to the observed data. The basic statistical properties of the observed and predicted data were compared. The results show that generated data preserve the basic statistical properties of the original series. (c) 2004 Elsevier B.V. All rights reserved.