Bayesian Regularized Back-Propagation Neural Network Model for Chlorophyll-a Prediction: A Case Study in Meiliang Bay, Lake Taihu

Abstract

A Bayesian regularized back-propagation neural network (BRBPNN) model is created and used to predict the monthly chlorophyll-a concentration dynamics over a period of 15 years in Meiliang Bay, Lake Taihu. The optimal network was found to consist of seven input neurons, six hidden neurons, and one output neuron, and coefficient of determination (R2) values for the training, validation, and test sets were 0.77, 0.49, and 0.76, respectively. Respective values of the root mean square (RMSE) and bias for the three data sets are 17.24 and −1.05 for training, 12.48 and 0.62 for validation, and 11.01 and 2.2 for testing. Compared with multiple linear regression models, the BRBPNN model fit the data much better. Thus, the BRBPNN model was shown to be a powerful tool for predicting the long-term chlorophyll-a concentration dynamics in Meiliang Bay. Furthermore, we find that algae in the Meiliang Bay, principally Microcystis, were alkalophilic, and phytoplankton production was controlled by P inputs from spring to early summer, whereas N played a more dominant controlling role in summer–fall. Therefore, reducing P may no longer be adequate for Lake Taihu, and new nutrient reduction strategies should incorporate N-input reduction along with P-input reductions.