This study focuses on predicting harmful algal bloom (HAB) events in Lake Okeechobee, a shallow lake in Florida. A spatio-temporal deep learning model is employed to predict the levels of cyanobacteria Microcystis aeruginosa (M. aeruginosa) present in the lake for a single-day and a 14day prediction horizon. Datasets collected from remote sensing (i.e., satellite images from Jan. 2018 to Dec. 2020) and from a physics-based simulation model (i.e., daily simulation from Jan. 2018 to Dec. 2020) are available. Due to the low quality of remote sensing data caused by various environmental and technical issues, the two available datasets are fused together to create a multi-source hybrid dataset for deep learning model training. A convolutional long-short term memory (ConvLSTM) deep neural model is trained on the datasets, and the results of the predictions are compared to the true Cyanobacterial Index (CI) for that time period. Findings include 1) the deep learning model, ConvLSTM, shows promising performance for short-and mid-term HAB forecasting; and 2) the hybrid dataset that fuses remote sensing with physics-based modeling (a.k.a. modeling based on fundamental physical and biogeochemical principles) speeds up the model learning and improves its performance significantly. The proposed methodologies are reliable, and costeffective, and could be used to forecast algal bloom occurrences in shallow lakes with limited sparse observations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.