Necati AĞIRALİOĞLU, Murat KÜÇÜK

Dalgacık dönüşüm tekniği kullanılarak hidrolojik akım serilerinin modellenmesi

Çalışmada işaret işleme sahasında kullanılan dalgacık dönüşümü tekniği hidrolojik akım serilerinin tahmininde kullanılmıştır, ilk olarak sürekli dalgacık dönüşümü ve global spektrum yardımı ile ölçüm serileri analiz edilmiştir. İki ayrı ölçüm istasyonuna ait akarsu akım serileri ayrık dalgacık dönüşümü uygulanarak bileşenlerine ayrılmıştır. Elde edilen bileşenler geliştirilen regresyon tipi bir model yardımı modellenmiştir. Modelde tahmin eden ve tahmin ettirici değişkenler yerine akım serilerinin ayrık dalgacık dönüşümü bileşenleri kullanılmıştır. Elde edilen sonuçlar farklı hata kriterleri ile değerlendirilmiştir. Modelleme sonuçlarında bölgenin iklim karakteristiğine uygun bileşenler arasında kurulan modellerin diğer modellere göre bir çok hata kriteri bakımından daha başarılı olduğu görülmüştür.

Modelling of streamflow series using wavelet transform technique

Even though earth science phenomena have nonstationary characteristics, they actually include many different secret periodic events occurred in different time periods. Wavelet transform gives better results than former techniques for analyzing these phenomena. In this study, a new modelling technique using discrete wavelet transform, which is a special type of wavelet transform, is presented to increase prediction performance of streamflow time series modelling as an earth science phenomena and to provide physical interpretation. Different periodic components of a streamflow series are always obtained using discrete wavelet transform. The present study differs in that discrete wavelet transform is firstly presented as a modelling technique with application on streamflow process. Instead of response and explanatory measured streamflow series in a linear regression model, discrete wavelet components of such series are used in this study. First of all, discrete wavelet components of streamflow series are analysed by continuous wavelet transform and global wavelet spectrum. Secondly, energy variation of each component of both measured series is calculated. If appropriate components obtained by discrete wavelet transform are used, the performance of streamflow models can be increased. This technique is applied to regression models, because they are simple and widely used in practical applications. Energy variation, correlation coefficients and error values of the constituted models are compared with each other. This new technique offers good advantage in streamflow modelling.

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