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

Yıl 2025, Cilt: 28 Sayı: 1, 1 - 18, 03.03.2025

Cihangir Köyceğiz , Meral Büyükyıldız

Öz

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.

Anahtar Kelimeler

Adaptif Destekleme Kızılırmak Mevsimsel Ayrıştırma Destek Vektör Makinesi Askıda Sediment Yükü

Kaynakça

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  • 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
  • Cai, Q. C., Hsu, T. H., & Lin, J. Y. (2021). Using the General Regression Neural Network Method to Calibrate the Parameters of a Sub-Catchment. Water, 13(8), 1089. https://doi.org/10.3390/w13081089
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EFFECT OF SEASONAL-TREND DECOMPOSITION ON MACHINE LEARNING-BASED SUSPENDED SEDIMENT LOAD PREDICTION PERFORMANCE

Yıl 2025, Cilt: 28 Sayı: 1, 1 - 18, 03.03.2025

Cihangir Köyceğiz , Meral Büyükyıldız

Öz

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)

Anahtar Kelimeler

Adaptive Boosting Kızılırmak Seasonal Decomposition Support Vector Machine Suspended Sediment Load

Teşekkür

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

Kaynakça

  • Acar, A. A. (2019). Kızılırmak havzasında yapay zekâ metotları kullanarak sediment taşınımının tahmini. Yüksek Lisans Tezi. Konya Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü İnşaat Mühendisliği Anabilim Dalı, Konya 89s.
  • Acar, R., & Saplıoğlu, K. (2022). Etkili girdi parametrelerinin çoklu regresyon ile belirlendiği su sertliğinin anfis yöntemi ile tahmin edilmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 22(6), 1413-1424. https://doi.org/10.35414/akufemubid.1147492
  • Adnan, R. M., Liang, Z., El-Shafie, A., Zounemat-Kermani, M., & Kisi, O. (2019). Prediction of Suspended Sediment Load Using Data-Driven Models. Water, 11(10), 2060. https://doi.org/10.3390/w11102060
  • 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
  • Cai, Q. C., Hsu, T. H., & Lin, J. Y. (2021). Using the General Regression Neural Network Method to Calibrate the Parameters of a Sub-Catchment. Water, 13(8), 1089. https://doi.org/10.3390/w13081089
  • Cigizoglu, H. K. (2005). Application of Generalized Regression Neural Networks to Intermittent Flow Forecasting and Estimation. Journal of Hydrologic Engineering, 10(4), 336–341. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:4(336
  • Cleveland, R., Cleveland, W., McRae, J., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on Loess. Journal of Official Statistics, 6(1), 3–73.
  • 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
  • Fang, K., Kifer, D., Lawson, K., Feng, D., & Shen, C. (2022). The data synergy effects of time‐series deep learning models in hydrology. Water Resources Research, 58(4), e2021WR029583. https://doi.org/10.1029/2021WR029583
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  • Özger, M., & Kabataş, M. B. (2015). Sediment load prediction by combined fuzzy logic-wavelet method. Journal of Hydroinformatics, 17(6), 930–942. https://doi.org/10.2166/hydro.2015.148
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  • Pandey, A., Himanshu, S. K., Mishra, S. K., & Singh, V. P. (2016). Physically based soil erosion and sediment yield models revisited. Catena, 147, 595-620. https://doi.org/10.1016/j.catena.2016.08.002
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
  • Piraei, R., Afzali, S. H., & Niazkar, M. (2023). Assessment of XGBoost to Estimate Total Sediment Loads in Rivers. Water Resources Management, 37(13), 5289–5306. https://doi.org/10.1007/s11269-023-03606-w
  • Samantaray, S., & Sahoo, A. (2022). Prediction of suspended sediment concentration using hybrid SVM-WOA approaches. Geocarto International, 37(19), 5609–5635. https://doi.org/10.1080/10106049.2021.1920638
  • Samantaray, S., Sahoo, A., & Ghose, D. K. (2020). Assessment of Sediment Load Concentration Using SVM, SVM-FFA and PSR-SVM-FFA in Arid Watershed, India: A Case Study. KSCE Journal of Civil Engineering, 24(6), 1944–1957. https://doi.org/10.1007/s12205-020-1889-x
  • Samantaray, S., Sahoo, A., Satapathy, D. P., Oudah, A. Y., & Yaseen, Z. M. (2024). Suspended sediment load prediction using sparrow search algorithm-based support vector machine model. Scientific Reports, 14 (1), 12889. https://doi.org/10.1038/s41598-024-63490-1
  • Sales, A. K., Gul, E., Safari, M. J. S., Ghodrat Gharehbagh, H., & Vaheddoost, B. (2021). Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm. Theoretical and Applied Climatology, 146(1), 833-849. https://doi.org/10.1007/s00704-021-03771-1
  • Saplıoğlu, K., & Acar, R. (2020). K-means kümeleme algoritması kullanılarak oluşturulan yapay zeka modelleri ile sediment taşınımının tespiti. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(1), 306-322. https://doi.org/10.17798/bitlisfen.558113
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  • Yilmaz, V., Koycegiz, C., & Buyukyildiz, M. (2024a). Performance of data-driven models based on seasonal-trend decomposition for streamflow forecasting in different climate regions of Türkiye. Physics and Chemistry of the Earth, Parts A/B/C, 136, 103696. https://doi.org/10.1016/j.pce.2024.103696
  • Yilmaz, V., Koycegiz, C., & Buyukyildiz, M. (2024b). An approach on the estimation and temporal interaction of runoff: the band similarity method. Journal of Water and Climate Change, 15 (9): 4775–4789. https://doi.org/10.2166/wcc.2024.420
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  • Yuan, Z., Gao, S., Wang, Y., Li, J., Hou, C., & Guo, L. (2023). Prediction of PM2. 5 time series by seasonal trend decomposition-based dendritic neuron model. Neural Computing and Applications, 35(21), 15397-15413. https://doi.org/10.1007/s00521-023-08513-0
  • Zhang, S., Wu, J., Jia, Y., Wang, Y. G., Zhang, Y., & Duan, Q. (2021). A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability. Engineering Applications of Artificial Intelligence, 100, 104206. https://doi.org/10.1016/j.engappai.2021.104206
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Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Su Kaynakları Mühendisliği
Bölüm İnşaat Mühendisliği
Yazarlar

Cihangir Köyceğiz 0000-0002-0510-1164

Meral Büyükyıldız 0000-0003-1426-3314

Yayımlanma Tarihi 3 Mart 2025
Gönderilme Tarihi 17 Haziran 2024
Kabul Tarihi 20 Aralık 2024
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 1

Kaynak Göster

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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