کنفرانس ملی مهندسی و مدیریت محیط زیست

صفحه اصلی / هفتمین کنفرانس ملی مهندسی و مدیریت محیط زیست

Process‑Informed Machine Learning for Satellite‑Based Prediction of Harmful Algal Blooms in the Persian Gulf

نویسندگان :

Zahra Mollaei¹ Soleyman Nezhadbasaidu²

1- دانشگاه سیستان و بلوچستان 2- دانشگاه سیستان و بلوچستان

کلمات کلیدی :

Red Tide،Persian Gulf،Machine Learning،Remote Sensing،XGBoost

چکیده :

Harmful algal blooms (HABs), commonly known as red tides, represent a major ecological and economic concern in the Persian Gulf, where high temperature, elevated salinity, and intense anthropogenic pressure create favorable conditions for bloom development. This study presents a Machine Learning (ML)–based framework for the monitoring and prediction of HABs using satellite‑derived biogeochemical indicators. Multi‑sensor data from MODIS‑Aqua and Sentinel‑3 OLCI (2018–2023) were processed to extract key environmental variables, including chlorophyll‑a (Chl‑a), sea surface temperature (SST), remote‑sensing reflectance (Rrs), photosynthetically active radiation (PAR), and the diffuse attenuation coefficient Kd(490). After preprocessing and labeling bloom‑prone pixels, four supervised algorithms—Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and eXtreme Gradient Boosting (XGBoost)—were trained to classify HAB versus non‑HAB conditions. Model performance was evaluated using Accuracy, F1‑score, Area Under the Curve (AUC), and Root Mean Square Error (RMSE). The results show that tree‑based models outperform other approaches, with XGBoost achieving the highest Accuracy (95.4%), F1‑score (0.94), AUC (0.97), and the lowest RMSE (1.05 mg m⁻³). These findings highlight the strong predictive capability of integrating satellite observations with advanced ML, offering a robust foundation for early‑warning and continuous monitoring systems to support sustainable marine ecosystem management in the Persian Gulf.