Spatio-Temporal Deep Learning-Based Algal Bloom Prediction for Lake Okeechobee Using Multi-Source Data Fusion

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing pp. 1 - 14
• Main Authors: Tang, Yufei, Feng, Yingqi, Fung, Sasha, Xomchuk, Veronica Ruiz, Jiang, Mingshun, Moore, Tim, Beckler, Jordon
• Format: Journal Article
• Language: English
• Published: IEEE 2022
• Subjects: Biological system modeling; Convolutional Long-Short Term Memory (ConvLSTM); Deep learning; deep learning modeling; harmful algal blooms (HABs); Lakes; multi-source data fusion; Predictive models; Remote sensing; Satellites; spatio-temporal prediction; Support vector machines
• ISSN: 1939-1404
• DOI: 10.1109/JSTARS.2022.3208620

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 14-day 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 cost-effective, and could be used to forecast algal bloom occurrences in shallow lakes with limited sparse observations.