We present fixed domain asymptotic results that establish consistent estimates of the variance and scale parameters for a Gaussian random field with a geometric anisotropic Matérn autocovariance in dimension d > 4. When d < 4 this is impossible due to the mutual absolute continuity of Matérn Gaussian random fields with different scale and variance (see Zhang [33]). Informally, when d > 4, we sh...

This article proposes and studies a link between statistics and the theory of Dirichlet forms used to compute errors. The error calculus based on Dirichlet forms is an extension of classical Gauss’ approach to error propagation. The aim of this paper is to derive error structures from measurements. The links with Fisher’s information lay the foundations of a strong connection with experiment. H...

We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted depth. Efficient, accurate, and trustworthy methods urban flood management have become increasingly important due higher rainfall intensity caused by climate change, expansion cities, changes i...

The slope length and steepness (LS) factor is one of the factors in the Revised Universal Soil Loss Equation (RUSLE) needed to estimate average annual erosion rate. The LS factor is often derived from digital elevation models (DEM). DEM errors and uncertainty could affect LS factor estimation and consequently erosion rate estimation. However, DEM uncertainties are not always accounted for, and ...