A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom
نویسندگان
چکیده
In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that effects on humans, fisheries, and ecosystems. this study, we developed compared efficiency state-of-the-art machine learning models (e.g., XGBoost, Random Forest, Support Vector Machine) predicting occurrence HABs. proposed K. abundance is used target, 10 level-02 ocean color products extracted from daily archival MODIS satellite data are controlling factors. The adopted approach addresses two main shortcomings earlier models: (1) paucity due to cloudy scenes (2) lag time between period at which a variable reaches its highest correlation with target bloom occurs. Eleven spatio-temporal were generated, each 3 consecutive day datasets, forecasting span 1 11 days. 3-day addressed potential variations for some temporal variables. One or more generated could be predict HAB occurrences depending availability cloud-free Findings indicate XGBoost outperformed other methods, 5–9 days achieved best results. reliable model can forecast eight ahead balanced overall accuracy, Kappa coefficient, F-Score, AUC 96%, 0.93, 0.97, 0.98 respectively. euphotic depth, sea surface temperature, chlorophyll-a always among significant potentially develop an “early warning system” HABs southwest Florida.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2072-4292']
DOI: https://doi.org/10.3390/rs13193863