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Leveraging Artificial Intelligence For Cyanobacterial Bloom Prediction A Hybrid Deep Learning and Ge by errykotor is a document available to read on EtoBox.
This study introduces an AI-based system for predicting cyanobacterial harmful algal blooms (CyanoHABs) using Long Short-Term Memory (LSTM) networks and One-Dimensional Convolutional Neural Networks (1D-CNNs). The system demonstrated high accuracy in predicting cyanobacterial density and bloom timing, achieving R² values of 98% and 88%, respectively. By leveraging Generative Adversarial Networks (GANs) for data augmentation, the framework aims to enhance proactive management of CyanoHABs, addressing signifi
- Author
- errykotor
- Language
- EN