KaydedilenlerDerginizi ekleyin

Dergiler / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2020 / Cilt: 38 - Sayı: 2

SUSPENDED SEDIMENT LOAD PREDICTION IN RIVERS BY USING HEURISTIC REGRESSION AND HYBRID ARTIFICIAL INTELLIGENCE MODELS

Banu YILMAZ
Egemen ARAS
Murat KANKAL
Sinan NACAR
Sayfa
703–714
Yayın Tarihi
2021-06-01
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It is concluded that these models of ANN method can be used successfully in estimating the SSL.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Accurate prediction of amount of sediment load in rivers is extremely important for river hydraulics.
📊
Temel Bulgular & Veri:The usage of different regression methods and artificial intelligence techniques allows the development of predictions as the traditional methods do not give enough accurate results.
💡
Sonuç & Katkı:It is concluded that these models of ANN method can be used successfully in estimating the SSL.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Artificial intelligence · Çoruh river basin · regression analysis

Abstract

Accurate prediction of amount of sediment load in rivers is extremely important for river hydraulics. The solution of the problem has been become complicated since the explanation of hydraulic phenomenon between the flow and the sediment on the river is dependent many parameters. The usage of different regression methods and artificial intelligence techniques allows the development of predictions as the traditional methods do not give enough accurate results. In this study, data of the flow and suspended sediment load (SSL) obtained from Karşıköy Gauging Station, located on Çoruh River in the north-eastern of Turkey, modelled with different regression methods (multiple regression, multivariate adaptive regression splines) and artificial neural network (ANN) (ANN-back propagation, ANN teaching-learning-based optimization algorithm and ANN-artificial bee colony). When the results were evaluated, it was seen that the models of ANN method were close to each other and gave better results than the regression models. It is concluded that these models of ANN method can be used successfully in estimating the SSL.

🔖

Bu Makaleye Atıf Yap

YILMAZ, B., ARAS, E., KANKAL, M., NACAR, S. (2021). SUSPENDED SEDIMENT LOAD PREDICTION IN RIVERS BY USING HEURISTIC REGRESSION AND HYBRID ARTIFICIAL INTELLIGENCE MODELS. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 38 - Sayı: 2, 703–714. https://ajindex.com/articles/418946

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (13)

  1. Olive, L.J., and Rieger, W.A., (1988) An examination of the role of sampling strategies inmthe study of the suspended sediment transport. Sediment Budgets, Proceedings of the Porto alegre Symposium. IAHS Publ. No. 174, 259-267
  2. Öztürk, F., (2002) Determination of Surface Flow and Sediment Amount by AGNPS Model (In Turkish), Scientific Research Project Final Report, Ankara University Scientific Research Projects, Ankara
  3. Kisi, Ö., Karahan M.E. ve Şen, Z., (2003) Fuzzy logic modeling of suspension material amount in rivers (In Turkish), İTÜ Journal, 2, 34, 43-54
  4. Yilmaz, B., Aras, E., Nacar, S., Kankal, M., (2018) Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Science of The Total Environment, 639, 826-840. 5] Jain, S. K., (2001) Development of integrated sediment rating curves using ANNs. Journal of Hydraulic Engineering, 127(1):30-37
  5. Tayfur, G., (2002) Artificial neural networks for sheet sediment transport. Hydrological Sciences Journal, 47(6): 879-892
  6. Cigizoglu, H. K., (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons, Advances in Water Resources 27(2):185-195
  7. Tayfur, G., and Guldal, V., (2006) Artificial neural networks for estimating daily total suspended sediment in natural streams. Hydrology Research 37(1):69-79
  8. Alp, M., and Cığızoğlu H. K., 2007. Suspended sediment load estimation by two artificial neural network methods using hydrometeorological data. Environmental Modelling & Software, 22, 2-13
  9. Zhu, Y. M., Lu, X. X., Zhou, Y., (2007) Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology 84(1):111-125
  10. Ardıclıoglu, M., Kişi, Ö., Haktanır, T., (2007) Suspended sediment prediction using two different feed-forward back-propagation algorithms. Canadian Journal of Civil Engineering 34(1):120-125
  11. Kisi, Ö., (2008) Constructing neural network sediment estimation models using a datadriven algorithm. Mathematics and Computers in Simulation 79(1):94-103
  12. Cobaner, M., Unal, B., and Kisi, O., (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. Journal of hydrology, 367(1-2), 52-61
  13. Rajaee, T., Mirbagheri, S. A., Zounemat-Kermani, M., Nourani, V., (2009) Daily susp