Journal
CLEAN-SOIL AIR WATER
Volume 38, Issue 10, Pages 969-976Publisher
WILEY
DOI: 10.1002/clen.201000068
Keywords
Alluvial channels; River engineering; Sediment transport; Support vector machine; Total sediment load
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Funding
- Department of Irrigation and Drainage Malaysia [CRNo JPS(PP)/SG/05/07]
- Universiti Sains Malaysia [1001/PREDAC/811077]
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In this study a novel machine learning technique called the support vector machine (SVM) method is proposed as a new predictive model to predict sediment loads in three Malaysian rivers The SVM is employed without any restriction to an extensive database compiled from measurements in the Muda Langat and Kurau rivers The SVM technique demonstrated a superior performance compared to other traditional sediment load methods The coefficient of determination 0 958 and the mean square error 0 0698 of the SVM method are higher than those of the traditional method The performance of the SVM method demonstrates its predictive capability and the possibility of the generalization of the model to nonlinear problems for river engineering applications
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