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

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

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 <italic>Microcystis aeruginosa</italic> present in the lake for a single-day and a 14-d...

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Bibliographic Details
Main Authors: Yufei Tang, Yingqi Feng, Sasha Fung, Veronica Ruiz Xomchuk, Mingshun Jiang, Tim Moore, Jordon Beckler
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional long-short term memory (ConvLSTM)
deep learning modeling
harmful algal blooms (HABs)
multisource data fusion
spatiotemporal prediction
Online Access:https://ieeexplore.ieee.org/document/9900398/

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https://ieeexplore.ieee.org/document/9900398/

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