Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques

Rocznik Ochrona Środowiska 2021, vol. 23, pp. 117-137

peopleFatih Üneş1, peopleBestami Taşar1 Ten adres pocztowy jest chroniony przed spamowaniem. Aby go zobaczyć, konieczne jest włączenie w przeglądarce obsługi JavaScript., peopleMustafa Demirci1, peopleMartina Zelenakova2, peopleYunus Ziya Kaya1, peopleHakan Varçin1 

institution  1. Civil Engineering Department, Iskenderun Technical University, Turkey
2. Environmental Engineering Institute, Kosice Technical University, Slovakia
mail author Ten adres pocztowy jest chroniony przed spamowaniem. Aby go zobaczyć, konieczne jest włączenie w przeglądarce obsługi JavaScript.
doi1 https://doi.org/10.54740/ros.2021.008
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abstract 


Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVM-PK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values.

 keywords


prediction, neuro-fuzzy, sediment rating curves, support vector machines, suspended sediment

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AMA Style
Üneş F, Taşar B, Demirci M, Zelenakova M, Ziya_Kaya Y, Varçin H. Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques. Rocznik Ochrona Środowiska. 2021; 23. https://doi.org/10.54740/ros.2021.008

ACM Style
Üneş, F., Taşar, B., Demirci, M., Zelenakova, M., Ziya_Kaya, Y., Varçin, H. 2021. Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques. Rocznik Ochrona Środowiska. 23. DOI:https://doi.org/10.54740/ros.2021.008

ACS Style
Üneş, F.; Taşar, B.; Demirci, M.; Zelenakova, M.; Ziya_Kaya, Y.; Varçin, H. Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques Rocznik Ochrona Środowiska 2021, 23, 117-137. https://doi.org/10.54740/ros.2021.008

APA Style
Üneş, F., Taşar, B., Demirci, M., Zelenakova, M., Ziya_Kaya, Y., Varçin, H. (2021). Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques. Rocznik Ochrona Środowiska, 23, 117-137. https://doi.org/10.54740/ros.2021.008

ABNT Style
ÜNEŞ, F.; TAŞAR, B.; DEMIRCI, M.; ZELENAKOVA, M.; ZIYA_KAYA, Y.; VARÇIN, H. Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques. Rocznik Ochrona Środowiska, v. 23, p. 117-137, 2021. https://doi.org/10.54740/ros.2021.008

Chicago Style
Üneş, Fatih, Taşar, Bestami, Demirci, Mustafa, Zelenakova, Martina, Ziya_Kaya, Yunus, Varçin, Hakan. 2021. "Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques". Rocznik Ochrona Środowiska 23, 117-137. https://doi.org/10.54740/ros.2021.008

Harvard Style
Üneş, F., Taşar, B., Demirci, M., Zelenakova, M., Ziya_Kaya, Y., Varçin, H. (2021) "Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques", Rocznik Ochrona Środowiska, 23, pp. 117-137. doi:https://doi.org/10.54740/ros.2021.008

IEEE Style
F. Üneş, B. Taşar, M. Demirci, M. Zelenakova, Y. Ziya_Kaya, H. Varçin, "Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques", RoczOchrSrod, vol 23, pp. 117-137. https://doi.org/10.54740/ros.2021.008