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Machine Learning Approach to Predict Sediment Load – A Case Study
Author(s) -
Azamathulla Hazi Md.,
Ghani Aminuddin Ab.,
Chang Chun Kiat,
Hasan Zorkeflee Abu,
Zakaria Nor Azazi
Publication year - 2010
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.201000068
Subject(s) - support vector machine , generalization , computer science , sediment , nonlinear system , artificial intelligence , machine learning , data mining , pattern recognition (psychology) , geology , mathematics , mathematical analysis , paleontology , physics , quantum mechanics
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.