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A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images

With the increasingly serious eutrophication of inland water, the frequency and scope of harmful cyanobacteria blooms are increasing, which affects the ecological balance and endangers human health. The aim of this study was to propose an alternative method for the quantification of cyanobacterial concentrations in water by correlating multispectral data. The research object was the cyanobacteria in Erhai Lake, Dali, China. Ten monitoring sites were selected, and multispectral images and cyanobacterial concentrations were measured in Erhai Lake from September to November 2021. In this study, multispectral data were used as independent variables, and cyanobacterial concentrations as dependent variables. We performed curve estimation, and significance analysis for the independent variables, and compared them with the original variable model. Here, we chose about four algorithms to establish models and compare their applicability, including Multivariable Linear Regression (MLR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Extreme Learning Machine (ELM). The prediction performance was evaluated by the coefficient of determination (R2), Root-Mean-Square Error (RMSE), and Mean Relative Error (MRE). The results showed that the variable analysis model outperformed the original variable model, the ELM was superior to other algorithms, and the variable analysis model based on the ELM algorithm achieved the best results (R2 = 0.7609, RMSE = 4197 cells/mL, MRE = 0.044). This study confirmed the applicability of cyanobacterial concentrations prediction using multispectral data, which can be characterized as a quick and easy methodology, and the deep neural network has great potential to predict the concentration of cyanobacteria.