Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir

Principle component regression (PCR), artificial neural network (ANN), and their combination used as data-driven models were selected to apply in this study to predict (based on the current-month variables) and forecast (based on the last 3-month-ahead variables) the phytoplankton dynamics in Macau Main Storage Reservoir (MSR) that is experiencing algal bloom in recent years. The models used the comprehensive 8 years' monthly water quality data for training and the most recent 3 years' monthly data for testing. Twenty-four water quality variables including physical, chemical, and biological parameters were involved, and comparisons were made to select the best models that can be applied to MSR. Simulation results revealed that ANN has better accuracy and generalization performance in comparison with PCR both for the prediction and the forecasted model. Using principal component analysis (PCA) for the data, inputs did not show better performance for the ANN, implying that eliminating the uncorrelated variables do not increase the prediction capability for the adopted model. Globally, in contrast with previous studies showing that the hybrid model can handle both linear and nonlinear components of the problems well, the PCR-ANN in this study obtain no better improvement.