187 citations · 249 across the 8 of their papers we have counts for
8 papers
SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
Dan Assouline, Erwan Koch, Federico Amato +5
Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that gl…
Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential
Federico Amato, Fabian Guignard, Alina Walch +3
The growth of wind generation capacities in the past decades has shown that wind energy can contribute to the energy transition in many parts of the world. Being highly variable an…
Uncertainty Quantification in Extreme Learning Machine: Analytical Developments, Variance Estimates and Confidence Intervals
Fabian Guignard, Federico Amato, Mikhail Kanevski
Uncertainty quantification is crucial to assess prediction quality of a machine learning model. In the case of Extreme Learning Machines (ELM), most methods proposed in the literat…
On Feature Selection Using Anisotropic General Regression Neural Network
Federico Amato, Fabian Guignard, Philippe Jacquet +1
The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models. Therefore, the development of featu…
A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning
Federico Amato, Fabian Guignard, Sylvain Robert +1
As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming…
Spatio-temporal evolution of global surface temperature distributions
Federico Amato, Fabian Guignard, Vincent Humphrey +1
Climate is known for being characterised by strong non-linearity and chaotic behaviour. Nevertheless, few studies in climate science adopt statistical methods specifically designed…
Advanced analysis of temporal data using Fisher-Shannon information: theoretical development and application in geosciences
Fabian Guignard, Mohamed Laib, Federico Amato +1
Complex non-linear time series are ubiquitous in geosciences. Quantifying complexity and non-stationarity of these data is a challenging task, and advanced complexity-based explora…
Analysis of air pollution time series using complexity-invariant distance and information measures
Federico Amato, Mohamed Laib, Fabian Guignard +1
Air pollution is known to be a major threat for human and ecosystem health. A proper understanding of the factors generating pollution and of the behavior of air pollution in time…