Our participation at EGU 2019
We actively participate in the European Geoscience Meeting (EGU) 2019, in Vienna.
Session convener:
- Machine learning for geosciences: data exploration and modelling
- Big data and machine learning in geosciences
- Learning from spatial data: unveiling the geo-environment through quantitative approaches
- Spatial and temporal patterns of wildfires: models, theory, and reality
Participation:
- Analysis of environmental time series complexity
- Fisher-Shannon Complexity of High-Frequency Wind Speed in Urban
area - Analysis of wind time series using network science and multifractal
concept - MFDFA R package: multifractal analysis for time series
- Feature Selection Inspired by Geospatial Data Analysis
- Feature selection using simple and efficient machine learning models.
Case studies and software tools - Application of Machine Learning for Wildfire Susceptibility Mapping in
Liguria (Italy) - Landslide susceptibility assessment using Machine Learning: the Valais
Canton (Switzerland) case study - The role of forest fires in land use/land cover changes in Portugal
