A Bayesian structural model for predicting algal blooms

A Bayesian structural model with two components is proposed to forecast the occurrence of algal blooms, multivariate mean-reverting diffusion process (MMRD), and a binary probit model with latent Markov regime-switching process (BPMRS). The model has three features: (a) forecast of the occurrence probability of algal bloom is directly based on oceanographic parameters, not the forecasting of special indicators in traditional approaches, such as phytoplankton or chlorophyll-a; (b) augmentation of daily oceanographic parameters from the data collected every 2 weeks is based on MMRD. The proposed method solves the problem of unavailability of daily oceanographic parameters in practice; (c) BPMRS captures the unobservable factors which affect algal bloom occurrence and therefore improve forecast accuracy. We use panel data collected in Tolo Harbour, Hong Kong, to validate the model. The model demonstrates good forecasting for out-of-sample rolling forecasts, especially for algal bloom appearing for a longer period, which severely damages fisheries and the marine environment.