Recent advances in algal bloom detection and prediction technology using machine learning.

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Harmful algal blooms (HABs) pose significant threats to aquatic ecosystems and human health.
• Traditional detection methods are labor-intensive, costly, and time-consuming.
• Machine learning (ML) presents a promising technological advancement for improved HAB detection and prediction.

Purpose of the Study:

• To provide a comprehensive overview of ML applications in HAB detection and prediction.
• To explore regression, classification, and image-based ML techniques for HAB analysis.
• To highlight real-world implementations and future research directions in ML for HAB management.

Main Methods:

• Utilized regression and classification models for HAB prediction based on water quality and environmental factors.
• Employed image-based ML techniques using satellite, surveillance, and microscopic imagery for algae detection.
• Reviewed existing literature and case studies on ML applications for HABs.

Main Results:

• ML models demonstrate enhanced accuracy and efficiency in detecting and predicting HABs.
• Image-based ML effectively identifies algae from various visual data sources.
• Explainable AI (XAI) aids in understanding environmental drivers of HABs, improving model interpretability.

Conclusions:

• ML technology significantly improves HAB detection and prediction, safeguarding ecosystems and public health.
• High-quality, representative data and robust data management are crucial for effective ML model performance.
• Future research should focus on enhancing ML model applicability and integrating XAI for informed decision-making in HAB management.