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MEVSİMSEL-TREND AYRIŞTIRMASININ MAKİNE ÖĞRENMESİ TABANLI ASKIDA SEDİMENT YÜKÜ TAHMİN PERFORMANSI ÜZERİNDEKİ ETKİSİ

Year 2025, Volume: 28 Issue: 1, 1 - 18, 03.03.2025

Cihangir Köyceğiz , Meral Büyükyıldız

Abstract

Sedimentin tahmin edilmesi, su kaynakları yönetimi için hayati önem taşımaktadır. Bu çalışmada, Kızılırmak Nehri'nin Bulakbaşı istasyonundaki askıda sediment yükünün (SSL) makine öğrenmesi tabanlı tahmin performansı araştırılmıştır. Ayrıca mevsimsel ayrıştırmanın tahmin performansı üzerindeki etkisi incelenmiştir. Bu doğrultuda, Destek Vektör Makinesi (SVM), Adaptif Boosting (AdaBoost) ve Genelleştirilmiş Regresyon Sinir Ağı (GRNN) algoritmaları SSL tahmini için kullanılmıştır. Hiperparametre optimizasyonu için Grid Search (GS) algoritması tercih edilmiştir. Mevsimsel bileşen, Mevsimsel-Trend ayrıştırması LOESS (STL) yöntemi kullanılarak elde edilmiştir. Akış (Qt), akış gecikmesi (Qt-1) ve SSL'nin mevsimsel bileşeni (S-SSLt) kullanılarak altı girdi kombinasyonu oluşturulmuştur. Bulgulara göre AdaBoost (M6-NSEEğitim=0,914, M4-NSETest=0,765), SVM (M6-NSEEğitim=0,912, M6-NSETest=0,863) ve GRNN (M6-NSEEğitim=0,912, M4-NSETest=0,834) modelleri oldukça tutarlı sonuçlar üretmiştir. Test aşamasında, SVM-M6 (R2=0,893, NSE=0,863) çeşitli değerlendirme ölçütlerine göre en başarılı modeldir. SSL'nin mevsimsel bileşeninin eklendiği son üç girdi kombinasyonunun genel olarak performansı artırdığı da gözlemlenmiştir. En başarılı model olan test aşamasındaki SVM için mevsimsel bileşenin olmadığı kombinasyonda (M3) R2=0,873, NSE=0,820 ve mevsimsel bileşenin olduğu kombinasyonda (M6) R2=0,893, NSE=0,863 değerleri elde edilmiştir.

Keywords

Adaptif Destekleme Kızılırmak Mevsimsel Ayrıştırma Destek Vektör Makinesi Askıda Sediment Yükü

References

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EFFECT OF SEASONAL-TREND DECOMPOSITION ON MACHINE LEARNING-BASED SUSPENDED SEDIMENT LOAD PREDICTION PERFORMANCE

Year 2025, Volume: 28 Issue: 1, 1 - 18, 03.03.2025

Cihangir Köyceğiz , Meral Büyükyıldız

Abstract

Forecasting of sediment is vital for water resources management. In this study, the machine learning-based prediction performance of suspended sediment load (SSL) at Bulakbaşı station of Kızılırmak River was investigated. Also, the effect of seasonal decomposition on the prediction performance was searched. Accordingly, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Generalized Regression Neural Network (GRNN) methods were used for SSL prediction. Grid Search (GS) algorithm was preferred for hyperparameter optimization. The seasonal component was obtained by Seasonal-Trend decomposition using the LOESS (STL) method. Six input combinations were generated using flow (Qt), flow lag (Qt-1), and the seasonal component of SSL (S-SSLt). According to the findings, AdaBoost (M6-NSETrain=0.914, M4-NSETest=0.765), SVM (M6-NSETrain=0.912, M6-NSETest=0.863), and GRNN (M6-NSETrain=0.912, M4-NSETest=0.834) models produced quite consistent results. In the test phase, SVM-M6 (R2=0.893, NSE=0.863) is the most successful model according to various evaluation metrics. It was also observed that the last three input combinations, where the seasonal component of SSL was added, generally improved the performance. For SVM in the test phase, which is the most successful model, R2=0.873, NSE=0.820 values were obtained in the combination without the seasonal component (M3), and R2=0.893, NSE=0.863 values were obtained in the combination with the seasonal component (M6)

Keywords

Adaptive Boosting Kızılırmak Seasonal Decomposition Support Vector Machine Suspended Sediment Load

Thanks

The authors would like to thank the General Directorate of State Hydraulic Works for the data used in this study

References

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  • Aghelpour, P., Graf, R., & Tomaszewski, E. (2023). Coupling ANFIS with ant colony optimization (ACO) algorithm for 1-, 2-, and 3-days ahead forecasting of daily streamflow, a case study in Poland. Environmental Science and Pollution Research, 30(19), 56440-56463. https://doi.org/10.1007/s11356-023-26239-3
  • AlDahoul, N., Essam, Y., Kumar, P., Ahmed, A. N., Sherif, M., Sefelnasr, A., & Elshafie, A. (2021). Suspended sediment load prediction using long short-term memory neural network. Scientific Reports, 11(1), 7826. https://doi.org/10.1038/s41598-021-87415-4
  • Asadi, M., Fathzadeh, A., Kerry, R., Ebrahimi-Khusfi, Z., & Taghizadeh-Mehrjardi, R. (2021). Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters. Arabian Journal of Geosciences, 14(18), 1–14. https://doi.org/10.1007/s12517-021-07922-6
  • Buyukyildiz, M., & Kumcu, S. Y. (2017). An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models. Water Resources Management, 31(4), 1343–1359. https://doi.org/10.1007/s11269-017-1581-1
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  • Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. https://doi.org/10.1080/01621459.1979.10481038
  • Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610. https://doi.org/10.1080/01621459.1988.10478639
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Details

Primary Language English
Subjects Water Resources Engineering
Journal Section Civil Engineering
Authors

Cihangir Köyceğiz 0000-0002-0510-1164

Meral Büyükyıldız 0000-0003-1426-3314

Publication Date March 3, 2025
Submission Date June 17, 2024
Acceptance Date December 20, 2024
Published in Issue Year 2025Volume: 28 Issue: 1

Cite

APA Köyceğiz, C., & Büyükyıldız, M. (2025). EFFECT OF SEASONAL-TREND DECOMPOSITION ON MACHINE LEARNING-BASED SUSPENDED SEDIMENT LOAD PREDICTION PERFORMANCE. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 1-18.
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