Algal Bloom Forecasting in a Classification and Regression Setting: Implementing a UNet Architecture to evaluate the differences between both settings

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...