Algal Bloom Forecasting in a Classification and Regression Setting
Implementing a UNet Architecture to evaluate the differences between both settings
R. Alvarez Lucendo (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A. Lengyel – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
J.C. van Gemert – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
R. Bruintjes – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
K.G. Langendoen – Graduation committee member (TU Delft - Embedded Systems)
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Abstract
Forecasting algal blooms using remote sensing data is less labour-intensive and has better cover- age in time and space than direct water sampling. The paper implements a deep learning technique, the UNet Architecture, to predict the chlorophyll concentration, which is a good indicator for al- gal bloom in the Rio Negro water reservoirs of Uruguay. The research question focuses on the dif- ferences between classification and regression in algal bloom forecasting. The experiments show that the regression implementation achieves bet- ter accuracy and lower mean squared error than the classification implementation that uses cross- entropy loss and four pre-fixed bins. Different loss functions that account for the class imbalance in the data do not improve the model’s performance. Fi- nally, a quantile-based binning strategy that consid- ers the data’s underlying distribution achieves the highest accuracy in both settings.