Results 101 to 110 of about 32,345 (261)
Kriging Methodology and Its Development in Forecasting Econometric Time Series [PDF]
One of the approaches for forecasting future values of a time series or unknown spatial data is kriging. The main objective of the paper is to introduce a general scheme of kriging in forecasting econometric time series using a family of linear ...
Andrej Gajdoš +2 more
doaj
Modelling spatial variability and uncertainty is a highly challenging subject in soil- and geosciences. Regression kriging (RK) has several advantages; nevertheless it is not able to model the spatial uncertainty of the target variable.
Gábor Szatmári +3 more
doaj +1 more source
We evaluated how landscape features, including fire, influence genetic connectivity for three small mammal species in northern Australian savannas. Using resistance surface modelling across two islands with contrasting disturbance regimes, we found that fire, rainfall, and topography affected gene flow in species‐ and scale‐specific ways.
Alexander R. Carey +7 more
wiley +1 more source
DCENT‐I: A Globally Infilled Extension of the Dynamically Consistent ENsemble of Temperature Dataset
DCENT‐I infills data gaps in DCENT, producing spatially coherent temperature fields (top) and a slightly higher GMST warming estimate (bottom). Top: December 1877 temperature anomalies (°C; 1961–1990 December baseline) from DCENT (left) and DCENT‐I (right). Bottom: GMST before (DCENT, blue) and after (DCENT‐I, red) infilling.
Duo Chan +8 more
wiley +1 more source
The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil ...
Shi-wen ZHANG +5 more
doaj +1 more source
Kriging for Interpolation in Random Simulation [PDF]
Whenever simulation requires much computer time, interpolation is needed. There are several interpolation techniques in use (for example, linear regression), but this paper focuses on Kriging.This technique was originally developed in geostatistics by D ...
Beers, W.C.M. van, Kleijnen, J.P.C.
core +1 more source
GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications
Global surface climate datasets contain structural uncertainty that is difficult to attribute to individual processing steps. We present GloMarGridding, a Python package that isolates the spatial interpolation component using Gaussian Process Regression (or kriging) to generate spatially complete fields and uncertainty estimates. The techniques used in
Richard C. Cornes +6 more
wiley +1 more source
ABSTRACT Background and Aims Inadequate complementary feeding practices (CFPs) can lead to malnutrition among children and result in poor health outcomes throughout life. This study aimed to explore the spatial patterns and determinants of inadequate complementary feeding indicators (CFIs) among children aged 6–23 months in Bangladesh.
Satyajit Kundu +5 more
wiley +1 more source
A Bayesian Geostatistical Approach to Analyzing Groundwater Depth in Mining Areas
This study addresses the spatial variability of groundwater levels within a mining basin in Greece. The objective is to develop an accurate spatial model of groundwater levels in the area to support an integrated groundwater management plan.
Maria Chrysanthi +2 more
doaj +1 more source
An informational approach to the global optimization of expensive-to-evaluate functions
In many global optimization problems motivated by engineering applications, the number of function evaluations is severely limited by time or cost. To ensure that each evaluation contributes to the localization of good candidates for the role of global ...
Département Signaux +5 more
core +5 more sources

