Machine learning and remote sensing technologies for algae monitoring and prediction in freshwater

Muhammad Sarfraz Ahmad Mangat, Teh Sabariah Abd Manan, Sharifah Sakinah Syed Abd Mutalib, Hiroki Matsuda, Yasuhiro Tawara, Rana Muhammad Mubeen Muhsin, Zarimah Hanafiah, Nur Liyana Mohd Kamal, Salmia Beddu, Daud Mohamad, Nur Azwa Muhamad Bashar, Wan Hanna Melini Wan Mohtar, Muhammad Raza Ul Mustafa, Mohamed Hasnain Isa, Hamidi Abdul Aziz, Mohd Suffian Yusoff

Abstract


Harmful algal blooms (HABs) in freshwater systems are increasingly driven by nutrient enrichment and climate change, posing serious ecological, health, and economic risks. Traditional monitoring methods, including field sampling and chlorophyll-a analysis, are labor-intensive, time-consuming, and spatially limited, making real-time assessment challenging. This review synthesizes recent advances in integrating remote sensing and machine learning (ML) for HAB detection, monitoring, and prediction. We highlight studies using satellites such as MODIS, Sentinel-2, Landsat-8, and Sentinel-3, and ML models including Random Forest, Support Vector Machine, Artificial Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory networks. These approaches allow accurate mapping of chlorophyll-a, phycocyanin, and cyanobacterial toxins, with R² values from 0.67 to 0.94 and predictive lead times up to one month. Explainable artificial intelligence (XAI) enhances model transparency and interpretability, supporting regional transferability and informed management decisions. We also review the ecological and human health impacts of HAB toxins, including microcystins, anatoxin-a, cylindrospermopsin, saxitoxins, and brevetoxins. Challenges such as data scarcity, ecological variability, and computational demands are discussed, alongside future directions focusing on hybrid ML models, autonomous sensing, and standardized datasets. Integrating remote sensing and ML offers a scalable, real-time, and interpretable framework for HAB management, contributing to public health protection and sustainable freshwater ecosystem management.

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References


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