A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores

Abstract

Chlorophyll-a is an established indexing marker for phytoplankton abundance and biomass amongst primary food producers in an aquatic ecosystem. Understanding and modeling the level of Chlorophyll-a as a function of environmental parameters have been found to be very beneficial for the management of the coastal ecosystems. This study developed a mathematical model to predict Chlorophyll-a concentrations based on a data driven modeling approach. The prediction model was developed using principal component analysis (PCA) and multiple linear regression analysis (MLR) approaches. The predictive success (R²) of the model was found to be ~84.8% for first approach and ~83.8% for the second approach. A final model was generated using a combined principal component scores (PCS) and MLR approach that involves fewer parameters and has a predictive ability of 83.6%. The PCS-MLR method helped to identify the relationship amongst dependent as well as predictor variables and eliminated collinearity problems. The final model is quite simple and intuitive and can be used to understand real system operations.

Keywords: Chlorophyll-a; Mathematical modeling; Multiple linear regression analysis; Prediction; Principle component analysis; Seawater quality.