The project is organised along two directions: 1) harnessing the power of machine learning algorithms to complete and process sparse and imbalanced data that we often encounter in environmental sciences and 2) designing indicators to qualify the ecological status of the considered environments. We will study the potential of interpolation algorithms in time and space as well as predictive models based on co-occurrences.
