Algal Bloom Forecasting: Classical Machine Learning versus Deep-Learning

The aim of this paper is to find out which Machine Learning (ML) model predicts the concentration of Chlorophyll-a, in the Palmar lake in Uruguay best. Currently there are no such models to predict the growth in this lake. The algorithms which will be compared in this paper are a Linear Regression model and the U-Net model. We will compare the...

Algal Bloom Forecasting using Remote Sensing with Spatially and Temporally Sparse Satellite Data

This research presents a method for forecasting algal blooms using remote sensing with spatially and temporally sparse satellite data. The method involves the use of multiple interpolation methods to interpolate the sparse input data. The approach is shown to be effective in predicting algal blooms in areas where data is sparse, and the results...