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A platform for research: civil engineering, architecture and urbanism
Machine Learning Approach to Predict Sediment Load – A Case Study
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.
Machine Learning Approach to Predict Sediment Load – A Case Study
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.
Machine Learning Approach to Predict Sediment Load – A Case Study
Azamathulla, Hazi Md. (author) / Ghani, Aminuddin Ab. (author) / Chang, Chun Kiat (author) / Hasan, Zorkeflee Abu (author) / Zakaria, Nor Azazi (author)
CLEAN – Soil, Air, Water ; 38 ; 969-976
2010-10-01
8 pages
Article (Journal)
Electronic Resource
English
Machine Learning Approach to Predict Sediment Load – A Case Study
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