Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow

Cenk SEZEN, (Ondokuz Mayıs Üniversitesi, İnşaat Mühendisliği Bölümü, Samsun, Türkiye)
Turgay PARTAL (Ondokuz Mayıs Üniversitesi, İnşaat Mühendisliği Bölümü, Samsun, Türkiye)
Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi
4 0
Yıl: 2021 Cilt: 8 Sayı: 1 Sayfa Aralığı: 228 - 240 Metin Dili: İngilizce DOI: 10.35193/bseufbd.878624 İndeks Tarihi: 29-11-2021

Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow

Öz:
The development of various models to estimate hydrological variables, such as precipitation and runoff issignificant regarding handling the water-related problems in the future. This study investigates the performancesof Artificial Neural Network (ANN), Auto-Regressive Integrated Moving Average (ARIMA), Wavelet-ARIMA(WARIMA), and WARIMA-ANN models for monthly streamflow forecasting. These models were utilized in twostations of the Susurluk basin in Turkey. In this regard, first, the streamflow data were decomposed intocomponents by wavelet transformation for the WARIMA and WARIMA-ANN models. After that, runoffpredictions were performed for each model. As comparison criteria, Root Mean Square Error (RMSE), KlingGupta Efficiency (KGE), and Nash Sutcliffe Efficiency (NSE) were taken into consideration. As a result, it wasobtained that WARIMA and WARIMA-ANN models performed better than the ARIMA and ANN models,particularly. In addition, it was seen that wavelet transformation improved the performance of ARIMA andARIMA-ANN models, obviously.
Anahtar Kelime:

Aylık Akış Tahmini için Stokastik, Yapay Sinir Ağı ve Dalgacık Bazlı Modellerin Kullanımı

Öz:
Yağış ve akış gibi hidrolojik verilerin tahmini için farklı modellerin geliştirilmesi gelecekte su ile ilgili problemlerle mücadele edebilmek açısından önemlidir. Bu çalışma, Yapay Sinir Ağı (ANN), Otoregresif Bütünleşik Hareketli Ortalama (ARIMA), Dalgacık-ARIMA (WARIMA) ve WARIMA-ANN modellerinin aylık akım tahmin performanslarını araştırmaktadır. Bu modeller, Türkiye’nin Susurluk havzasındaki iki istasyonda uygulanmıştır. Bu bağlamda, ilk olarak akış verileri WARIMA ve WARIMA-ANN modelleri için dalgacık dönüşümü ile bileşenlerine ayrılmıştır. Daha sonra, her bir model için akış tahminleri gerçekleştirilmiştir. Karşılaştırma ölçütü olarak, Hataların Ortalama Karakökü (RMSE), Kling-Gupta Verimliliği (KGE) ve NashSutcliffe Verimliliği (NSE) göz önünde bulundurulmuştur. Sonuç olarak, WARIMA ve WARIMA-ANNmodellerinin, özellikle ARIMA ve ANN modellerine göre daha iyi performans gösterdiği tespit edilmiştir.Bunaek olarak, dalgacık dönüşümünün ARIMA ve ARIMA-ANN modellerinin performansını geliştirdiği belirgin şekilde görülmüştür.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA Sezen C, PARTAL T (2021). Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. , 228 - 240. 10.35193/bseufbd.878624
Chicago Sezen Cenk,PARTAL Turgay Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. (2021): 228 - 240. 10.35193/bseufbd.878624
MLA Sezen Cenk,PARTAL Turgay Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. , 2021, ss.228 - 240. 10.35193/bseufbd.878624
AMA Sezen C,PARTAL T Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. . 2021; 228 - 240. 10.35193/bseufbd.878624
Vancouver Sezen C,PARTAL T Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. . 2021; 228 - 240. 10.35193/bseufbd.878624
IEEE Sezen C,PARTAL T "Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow." , ss.228 - 240, 2021. 10.35193/bseufbd.878624
ISNAD Sezen, Cenk - PARTAL, Turgay. "Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow". (2021), 228-240. https://doi.org/10.35193/bseufbd.878624
APA Sezen C, PARTAL T (2021). Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 8(1), 228 - 240. 10.35193/bseufbd.878624
Chicago Sezen Cenk,PARTAL Turgay Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 8, no.1 (2021): 228 - 240. 10.35193/bseufbd.878624
MLA Sezen Cenk,PARTAL Turgay Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, vol.8, no.1, 2021, ss.228 - 240. 10.35193/bseufbd.878624
AMA Sezen C,PARTAL T Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi. 2021; 8(1): 228 - 240. 10.35193/bseufbd.878624
Vancouver Sezen C,PARTAL T Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi. 2021; 8(1): 228 - 240. 10.35193/bseufbd.878624
IEEE Sezen C,PARTAL T "Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow." Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 8, ss.228 - 240, 2021. 10.35193/bseufbd.878624
ISNAD Sezen, Cenk - PARTAL, Turgay. "Utilization of Stochastic, Artificial Neural Network, andWavelet Combined Models for Monthly Streamflow". Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 8/1 (2021), 228-240. https://doi.org/10.35193/bseufbd.878624
Sayfa Oluşturulma Tarihi
23-04-2024 20:21

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