Earth Observation–Based Machine learning for operational forecasting of harmful algal blooms in the Arabian Gulf and Sea of Oman

Abstract

Harmful algal blooms (HABs) pose increasing risks to coastal ecosystems, fisheries, and desalination infrastructure in the Arabian Gulf and Sea of Oman. Forecasting bloom occurrence remains challenging due to strong spatial variability in physical forcing and biological response. Here, we develop a physics-informed machine-learning framework to forecast HAB probability using satellite Earth observation data and reanalysis-derived environmental predictors over the period 2002–2024. Bloom events are defined using region-specific, seasonally varying chlorophyll thresholds derived from training data, and a Random Forest classifier is used to estimate bloom probability at 8-day resolution. Operational probability thresholds are calibrated using historical data (2002–2020) and applied unchanged to independent evaluation (2021–2022) and strictly forward (2023–2024) periods. The model demonstrated moderate classification skill during the independent evaluation period (F1 = 0.46–0.67) and strong forward operational performance, achieving F1 scores of 0.79–0.97 across the study regions. Permutation importance analysis showed that chlorophyll anomaly contributed most strongly to forecast performance across regions, indicating that deviations from seasonal biomass conditions are strongly associated with bloom detection skill, while physical variables provide complementary predictive information. These findings demonstrate that a biomass-informed, Earth observation–based framework enables robust and operationally stable detection of harmful algal blooms across hydrographically complex marginal seas, supporting its application as an early-warning system for HAB monitoring and management in the Arabian Gulf and Sea of Oman.