Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs

Chlorophyll-a is a well-accepted index for phytoplankton abundance and population of primary producers in an aquatic environment. The relationships between Chlorophyll-a and 16 chemical, physical and biological water quality variables in Çamlıdere reservoir (Ankara, Turkey) were studied by using principal component scores (PCS) in multiple linear regression analysis (MLR) to predict Chlorophyll-a levels. Principal component analysis was used to simplify the complexity of relations between water quality variables. Score values obtained by PC scores were used as independent variables in the multiple linear regression models. Two approaches were used in the present statistical analysis. In the first approach, only five selected score values obtained by PC analysis were used for the prediction of Chlorophyll-a levels and predictive success (R2) of the model found as 56.3%. In the second approach, where all score values obtained from the PC analysis were used as independent variables, predictive power was turned out to be 90.8%. Both approaches could be used to predict Chlorophyll-a levels in reservoirs successfully.