Prediction Of Chlorophyll-a As an Index of Harmful Algal Blooms Using Machine Learning Models
Ibrahim Busari,
Debabrata Sahoo,
R. Daren Harmel
et al.
Abstract:Highlights
The monitoring of HABs can be improved using ML models for chlorophyll-a prediction.
ML model selection for HABs monitoring depends on target objectives.
Random forest model predicts chlorophyll-a better when the temporal dimension is not considered.
The LSTM model is essential for making time-dependent chlorophyll-a predictions for HABs monitoring.
Abstract. Th… Show more
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