Object

Title: Daily Suspended sediment prediction using seasonal time series and artificial intelligence techniques

Description:

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.

Place of publishing:

Koszalin

Publisher:

Politechnika Koszalińska

Format:

application/pdf

Identifier:

oai:dlibra.tu.koszalin.pl:1649

Language:

eng

Is part of:

Rocznik Ochrona Środowiska. Vol. 23, s. 117-137

Rights:

Biblioteka Politechniki Koszalińskiej

Access rights:

internet

License:

Creative Commons BY-SA 4.0

Object collections:

Last modified:

Nov 6, 2023

In our library since:

Oct 11, 2022

Number of object content hits:

30

All available object's versions:

https://dlibra.tu.koszalin.pl/publication/1653

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