Prediction model of roughness coefficient of artificially roughened channels based on principal component analysis-support vector machine
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Abstract:
With the rectangular artificially roughened channel as the research object, we established a prediction model of roughness coefficient using the principal component analysis-support vector machine method. According to the preliminary experimental results, we selected four main influence factors: Froude number Fr, absolute roughness△, channel average water depth h, and bottom slope i. We used the principal component analysis method to obtain two main components, and obtained the comprehensive indexes influencing roughness coefficient, and used them for data training , testing, and prediction of the support vector machine. The research results showed that the RMSE and prediction correlation coefficient R of the training setwere 3.85×10﹣4 and 0.997 respectively, while those of the test set were and 0.992 respectively. The relative error was less than 5% . The results showed that the model based on principal component analysis-support vector machine is suitable for predicting the roughness coefficient of artificial channels.
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