Abstract
ABSTRACT Predicting sediment transport in a river system is necessary when attempting to monitor river changes due to incoming particle deposition from upstream reaches. Previous studies have used various techniques such as probabilistic, regression, and equal mobility approaches to make predictions. However, issues associated with multicollinearity occur when using the regression approach. Significant correlations between the explanatory variables in a regression equation can increase the coefficient variance estimates, which make these estimates very sensitive to minor changes. Thus, we propose the use of principal component analysis as a solution to these issues. We observed that the variance inflation factor is less than 10, which indicates the nonexistence of issues associated with multicollinearity. Fitting components to the regression equation create a new prediction model for sediment transport, which improves upon previous equations using the same technique.
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