Archive

Published: Građevinar 66 (2014) 3
Paper type: Preliminary report
Download article (Croatian): PDF
Download article (English): PDF
View count: 246

Suspended sediment modelling by SVM and wavelet

Maedeh Sadeghpour Haji, Seyed Ahmad Mirbagheri, Amir H. Javid, Mostafa Khezri, Ghasem D. Najafpour

Abstract

Present-day advances in artificial intelligence, as a forecaster for hydrological events, have led to numerous changes in forecasting. The wavelet support vector machine (WSWM) model is achieved by conjunction of the wavelet analysis and the support vector machine (SVM). The suspended sediment (SS) and daily stream flow (Q) data from the Iowa River in the USA were used for training and testing. The WSVM could logically be used for approximation of the suspended sediment load.

Keywords
support vector machine method, Discrete wavelet analysis, Cumulative SS, Daily stream flow, High suspended sediment

HOW TO CITE THIS ARTICLE:

Sadeghpour Haji, M., Mirbagheri, S. A., Javid, A. H., Khezri, M., Najafpour, G. D.: Suspended sediment modelling by SVM and wavelet, GRAĐEVINAR, 66 (2014) 3, pp. 211-223, doi: https://doi.org/10.14256/JCE.981.2013

OR:

Sadeghpour Haji, M., Mirbagheri, S. A., Javid, A. H., Khezri, M., Najafpour, G. D. (2014). Suspended sediment modelling by SVM and wavelet, GRAĐEVINAR, 66 (3), 211-223, doi: https://doi.org/10.14256/JCE.981.2013

LICENCE:

Creative Commons License
This paper is licensed under a Creative Commons Attribution 4.0 International License.
Authors:
Maedeh Sadeghpour Haji WEB
Maedeh Sadeghpour Haji
Environment and Energy, Science and Research Branch,
Islamic Azad University,
Tehran, Iran

Seyed Ahmad Mirbagheri WEB
Seyed Ahmad Mirbagheri
K. N. Toosi University of Technology
Department of Civil and Environmental Engineering
Amir H Javid WEB
Amir H. Javid
Islamic Azad University
Science and Research Branch
Mostafa Khezri WEB
Mostafa Khezri
Islamic Azad University
Science and Research Branch
Ghasem D Najafpour WEB
Ghasem D. Najafpour
Babol Noshirvani University of Tech.
Biotechnology Research Center