1 code implementation • 25 Nov 2023 • Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, Madeleine Pascolini-Campbell
We develop a machine learning model to predict post-fire burn severity using pre-fire remotely sensed data.
no code implementations • 16 Jul 2023 • Pratik Nag, Ying Sun, Brian J Reich
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.
no code implementations • 20 Jun 2023 • Pratik Nag, Ying Sun, Brian J Reich
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal mod-elling and prediction.
no code implementations • 22 Dec 2020 • Yawen Guan, Garritt L. Page, Brian J Reich, Massimo Ventrucci, Shu Yang
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