Wyniki wyszukiwania - BazTech

Ograniczanie wyników

1 Foundations of Computing and Decision Sciences

1 2025

Znaleziono wyników: 1

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: CyanoHABs

Sortuj według:

Ogranicz wyniki do:

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

Dendani Nadjette, Saoudi Amel, Amara Nour Djihane, Azizi Nabiha, Dugdale Julie, Hadiby Rayenne

Foundations of Computing and Decision Sciences

2025

Vol. 50, No. 4

427--450

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 (R²) 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.