Leveraging Artificial Intelligence for Cyanobacterial Bloom Prediction: A Hybrid Deep Learning and Generative Adversarial Network Framework for Accurate Forecasting and Proactive Management
Authors
Nadjette Dendani
LabGed Laboratory, Computer Science Department, Badji Mokhtar University, Annaba, Algeria
Amel Saoudi
Ecobiology Laboratory for Marine Environments and Coastal Areas,Biochemistry Department, Badji Mokhtar University, Annaba, Algeria
Nour Djihane Amara
LabGed Laboratory, Computer Science Department, Badji Mokhtar University, Annaba, Algeria
Nabiha Azizi
LabGed Laboratory, Computer Science Department, Badji Mokhtar University, Annaba, Algeria
Julie Dugdale
julie.dugdale@univ-grenoble-alpes.fr
Grenoble Informatics Laboratory, Grenoble Alps University, France
Language: English
Page range: 427 - 450
Submitted on: Feb 25, 2025
Accepted on: Nov 5, 2025
Published on: Dec 8, 2025
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
© 2025 Nadjette Dendani, Amel Saoudi, Nour Djihane Amara, Nabiha Azizi, Julie Dugdale, Rayenne Hadiby, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.