Spatiotemporal Deep-Learning-Based Algal Bloom Prediction for Lake Okeechobee Using Multisource Data Fusion

Abstract

This study focuses on predicting harmful algal bloom (HAB) events in Lake Okeechobee, a shallow lake in Florida. A spatiotemporal deep learning model is employed to predict the levels of cyanobacteria Microcystis aeruginosa present in the lake for a single-day and a 14-day prediction horizon. Datasets collected from remote sensing (i.e., satellite images from January 2018 to December 2020) and from a physics-based simulation model (i.e., daily simulation from January 2018 to December 2020) are available. Owing 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 multisource 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 for that time period. Findings include the following: 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 cost-effective and could be used to forecast algal bloom occurrences in shallow lakes with limited sparse observations.

Keywords

• Convolutional long-short term memory (ConvLSTM)
• deep learning modeling
• harmful algal blooms (HABs)
• multisource data fusion
• spatiotemporal prediction