Results 51 to 60 of about 9,305 (198)
Bootstrap based uncertainty bands for prediction in functional kriging [PDF]
, 2016 The increasing interest in spatially correlated functional data has led to the development of appropriate geostatistical techniques that allow to predict a curve at an unmonitored location using a functional kriging with external drift model that takes ...
Franco-Villoria, Maria +1 more
core +3 more sources
Study of the spatial variability of moisture and compaction in soils with different plant covers
Agronomía Colombiana, 2016 Soil is a dynamic system, with physical, chemical and biological properties that have high spatial variability, making necessary to use innovative methodologies to study this variability.
Lida Paola Pinzón-Gómez +2 more
doaj +1 more source
Spatial risk measures and applications to max-stable processes
, 2016 The risk of extreme environmental events is of great importance for both the authorities and the insurance industry. This paper concerns risk measures in a spatial setting, in order to introduce the spatial features of damages stemming from environmental
Koch, Erwan
core +1 more source
Fast semivariogram computation using FPGA architectures
SPIE Proceedings, 2015 The semivariogram is a statistical measure of the spatial distribution of data and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas.
Yamuna Lagadapati +2 more
openaire +2 more sources
Environmetrics, Volume 37, Issue 1, January 2026.ABSTRACT Classic Bayesian methods with complex environmental models are frequently infeasible due to an intractable likelihood. Simulation‐based inference methods, such as neural posterior estimation, calculate posteriors without accessing a likelihood function by leveraging the fact that data can be quickly simulated from the model, but converge ...
Elliot Maceda +3 more
wiley +1 more source
Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland [PDF]
, 2011 Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data.
Berenguer, M. +5 more
core +2 more sources
PLANTS, PEOPLE, PLANET, Volume 8, Issue 1, Page 338-352, January 2026.Cities are getting hotter because of climate change and urban development, increasing risks to health and well‐being. We analyzed how increasing urban tree canopy cover in city areas of 900 m2 can reduce land surface temperatures, using detailed aerial‐LiDAR and satellite data with Bayesian hierarchical models.
Ángel Ruiz‐Valero +7 more
wiley +1 more source
MULTIVARIATE SIMULATION OF CHANNEL IRON ORE DEPOSITS AT BUNGAROO AND YANDICOOGINA, WESTERN AUSTRALIA [PDF]
, 2007 Geostatistical conditional simulation has wide potential applications in the iron ore industry and is the favoured tool to assess variability and risk.
Boyle, Cameron McLaren Wilson
core +1 more source
Vadose Zone Journal, Volume 25, Issue 1, January/February 2026.Abstract Wind erosion is a major threat to organic soils under intensive agriculture, reducing their productivity and long‐term sustainability. Up to 2.5 cm of soil can be lost in a single storm. This study examined how soil water content and vegetation cover influence wind erosion in cultivated organic soils of the Montérégie region, Quebec, using ...
Saba Daeichin +4 more
wiley +1 more source
A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction [PDF]
, 2014 In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the final regression analysis.
Bai, Lianyang +5 more
core +1 more source