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Leveraging Artificial Intelligence for Cyanobacterial Bloom Prediction: A Hybrid Deep Learning and Generative Adversarial Network Framework for Accurate Forecasting and Proactive Management

Domain:

environment and energyhealthcare

Record type:

paper
Creator:
NadAmeNouNab
Publisher:
Wal
Host:
Abstract This study presents an Artificial Intelligence-based system designed to predict cyanobacterial harmful algal blooms (CyanoHABs). The system utilizes Long Short-Term Memory (LSTM) networks to predict the timing of bloom occurrences and One-Dimensional Convolutional Neural Networks (1D-CNNs) to estimate cyanobacterial density. Additionally, Generative Adversarial Networks (GANs) are employed for data augmentation to enrich the database. The system’s performance was validated using the Algerian Mexa database, achieving an R-squared (R 2 ) value of 98% and a root mean square error (RMSE) of 9% for cyanobacterial density prediction, and an R-squared value of 88% with a root mean square error of 31% for bloom timing prediction. These results highlight the system’s robust predictive capabilities, enabling proactive monitoring and management of CyanoHABs to mitigate their adverse impacts on health and the environment.

Visit

doi.org

Languages

Arabic, Algerian Spoken

Licenses

http://creativecommons.org/licenses/by-nc-nd/3.0

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