Evaluation of Machine Learning predictions of a highly resolved long time series of Chlorophyll-a concentration
F. de Luca Lopes de Amorim, J. Rick, G. Lohmann, K. H. Wiltshire
The paper evaluates Machine Learning models for predicting Chlorophyll-a concentrations using environmental data and finds Support Vector Machine Regressor performs best with improved accuracy through feature engineering.
bioRxiv · May 12, 2021Ecology