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

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.