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Found 4 papers, 2 papers with code
Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential
These include (i) insufficient consideration of spatio-temporal correlations in wind-speed data, (ii) a lack of existing methodologies to quantify the uncertainty of wind speed prediction and its propagation to the wind-power estimation, and (iii) a focus on less than hourly frequencies.
Uncertainty Quantification in Extreme Learning Machine: Analytical Developments, Variance Estimates and Confidence Intervals
Uncertainty quantification is crucial to assess prediction quality of a machine learning model.
On Feature Selection Using Anisotropic General Regression Neural Network
The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models.
A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning
As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis.
