Early Detection of Harmful Algal Blooms Using Advanced Deep Learning Techniques

Abstract

Harmful algal blooms (HABs) are rapidly developing aquatic phenomena that can degrade water quality, disrupt aquatic ecosystems, threaten fisheries, and create risks to human health. Conventional HAB monitoring generally depends on field sampling and laboratory analysis, which can be expensive, time-consuming, and limited in spatial and temporal coverage. Remote sensing has therefore emerged as an effective approach for observing bloom-related changes over large aquatic regions, while recent advances in deep learning provide new opportunities for automated interpretation of complex multispectral and temporal observations . This study proposes an advanced deep learning framework for the early detection of harmful algal blooms using remotely sensed environmental data. The proposed approach is designed to learn discriminative spatial, spectral, and temporal characteristics associated with bloom formation and distinguish emerging HAB conditions from normal water conditions. Deep neural architectures, including convolutional and temporal learning components, can be employed to extract hierarchical features from satellite observations and associated environmental variables. The framework further considers pre-processing, feature representation, model training, and early-warning classification to improve the reliability of HAB identification. By integrating remote sensing with deep learning, the proposed approach aims to provide timely and scalable bloom surveillance and support proactive aquatic ecosystem management. The study also addresses important challenges such as class imbalance, cloud-contaminated observations, variations in water conditions, and the generalization of trained models across different geographic regions. The resulting system is intended to provide a foundation for intelligent, data-driven HAB early-warning applications.