Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: A case study of the Yeongsan Reservoir, Korea
- Chl-a is used as the main dependent variable because of its simple, direct, and reasonable indicator for measuring phytoplankton biomass
- The existence of correlation among explanatory variables (“collinearity”), causes the poor accuracy of prediction model
- Main Topic
- The backward stepwise method of MLR is useful for solving the collinearity problem?
- Can PCR be used as an alternative approach for resolving the collinearity problem?
- What is the best regression model with the least uncertainty?
- Complexity and noise in the original data were removed by PCR
- F-overall-number of explanatory variables (RFN) curve, the proposed methodology selected four regression models as a basis(candidates) for further comparison of expanded uncertainties.
Sung Ho Shin (Ph.D. program)
Environmental Systems Engineering Lab.
School of Earth Sciences and Environmental Engineering
Gwangju Institute of Science and Technology
1 Oryong-dong Buk-gu Gwangju, 500-712, Korea
Phone : +82-10-6634-8614
E-mail : firstname.lastname@example.org
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