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Found 4 papers, 1 papers with code
Modelling wildland fire burn severity in California using a spatial Super Learner approach
We develop a machine learning model to predict post-fire burn severity using pre-fire remotely sensed data.
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields
Large-scale spatial interpolation or downscaling of bivariate wind fields having velocity in two dimensions is a challenging task because wind data tend to be non-Gaussian with high spatial variability and heterogeneity.
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal mod-elling and prediction.
A spectral adjustment for spatial confounding
We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the treatment variable to the mean of the response variable.
Methodology
