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Günlük akımların tahmini açısından çok tabakalı perseptron ve dalgacık-çok tabakalı perseptron modellerinin performans karşılaştırması

Performance comparison of multilayer perceptron and wavelet-multilayer perceptron models in terms of daily streamflow prediction

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Abstract (2. Language): 
Accurate streamflow prediction is important for sustainable water resources management. Direct use of observed data in developing prediction models has resulted in inaccuracies and predictions are with short lead times. DWT was used to decompose the observed data into components with the objective of enhancing the prediction accuracy and prediction lead times. The decomposed data were used as input into multilayer perceptron (MP) to develop a new approach for predicting daily streamflow for lead times up to 7 days. The new approach was called Wavelet-Multilayer Perceptron (W-MP). Twelve years of approved daily streamflow data were obtained from Station 02231000, USA. Seven years of data were used for calibration and the remaining 5 years of data were used for prediction. The new approach was compared to the stand-alone MP model by taking root mean squared error, coefficient of efficiency and skill score into consideration. The results showed that the W-MP model performed better than the stand-alone MP model and the prediction accuracy increased with the use of decomposed signals up to prediction lead time of 4 days. This indicates that the W-MP model can predict daily streamflow better than MP with extended lead time.
Abstract (Original Language): 
Akarsu akımlarının doğru tahmini su kaynaklarını sürekli ve iyi bir şekilde işletmek için önemlidir. Modellerde ölçülen ham verilerin doğrudan kullanımı, tahmin yanlışlıklarına neden olmaktadır. Modellerin tahmin performansını artırmak için ölçülen verileri spektral bantlara ayrıştırarak, trendleri ve periyodikliği ortadan kaldırmak için Ayrık Dalgacık Dönüşüm (ADD) yaklaşımı kullanılmıştır. Literatürde yapılan çalışmaların birçoğunda günlük akım tahminleri kısa süreli yapılmıştır. Bu çalışmada ADD kullanılarak orjinal veriler bileşenlerine ayrılarak model geliştirilmiştir. Bu çalışmanın amacı günlük akım gözlem verilerini doğru ve uzun süreli tahmin edebilen bir model geliştirmektir. Bu çalışma da, oniki (12) yıllık 02231000 nolu istasyona ait günlük akım gözlem verileri kullanılmıştır. Bunlardan, 7 yılı eğitme verisi geri kalan 5 yıl da tahmin verisi olarak kullanılmıştır. ADD yaklaşımı tarafından ayrıştırılan veriler, 7 güne kadar günlük akımları tahmin etmek için Çok Tabakalı Perseptron (ÇTP) modeline girdi olarak kullanılmıştır. Bu çalışmada ÇTP ve D-ÇTP modellerinin tahmin performansları Hataların karelerinin ortalamalarının karekökü (HKOK), verim katsayısı (VK) ve beceri puanı (BP) parametreleri dikkate alınarak karşılaştırılmıştır. D-ÇTP modellerinin ÇTP modelinden daha iyi tahmin sonuçları ürettiği gözlemlenmiştir.



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