Harmful Algal Bloom

Harmful algal blooms (HABs) are sudden increases in algae populations that can produce toxins harmful to marine life and humans, impacting aquaculture and public health. Current research focuses on developing automated monitoring systems using machine learning, particularly employing convolutional neural networks (CNNs) like ResNet and generative adversarial networks (GANs) for image analysis and prediction models such as random forests and hybrid neural network approaches for forecasting bloom occurrences and toxicity levels. These advancements aim to improve early warning systems, optimize shellfish harvesting practices, and ultimately mitigate the significant economic and health consequences associated with HABs.