Results 111 to 120 of about 543,708 (320)
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.
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More Efficient Prediction for Ordinary Kriging to Solve a Problem in the Structure of Some Random Fields [PDF]
, 2022 Mohammad Mehdi Saber, Ramy Aldallal
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Digital mapping of soil erodibility: A case study of the Ravang watershed, southern Iran
Agrosystems, Geosciences &Environment, Volume 9, Issue 1, March 2026.Abstract The Universal Soil Loss Equation incorporates soil erodibility as a key parameter for erosion quantification. This study focused on mapping soil erodibility patterns and identifying the primary factors influencing its spatial distribution within the Ravang watershed, located in southern Iran's Hormozgan Province.
Fahimeh Torkamani +3 more
wiley +1 more source
Bayesian analysis of hierarchical multi-fidelity codes [PDF]
, 2012 This paper deals with the Gaussian process based approximation of a code which can be run at different levels of accuracy. This method, which is a particular case of co-kriging, allows us to improve a surrogate model of a complex computer code using fast
Gratiet, Loic Le
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A Bayesian Spatiotemporal Functional Model for Data With Block Structure and Repeated Measures
Environmetrics, Volume 37, Issue 2, March 2026.ABSTRACT The analysis of spatiotemporal data is fundamental across multiple scientific disciplines, particularly in assessing the behavior of climate effects over space and time. A key challenge in this area is effectively capturing recurring climate phenomena, such as El Niño/La Niña (ENSO) phases, which induce prolonged periods of similar weather ...
David H. da Matta +3 more
wiley +1 more source
Conformal prediction for functional Ordinary kriging
Functional Ordinary Kriging is the most widely used method to predict a curve at a given spatial point. However, uncertainty remains an open issue. In this article a distribution-free prediction method based on two different modulation functions and two conformity scores is proposed.
De Magistris, Anna +2 more
openaire +2 more sources
Bayesian Inference for Spatially‐Temporally Misaligned Data Using Predictive Stacking
Environmetrics, Volume 37, Issue 2, March 2026.ABSTRACT Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly
Soumyakanti Pan, Sudipto Banerjee
wiley +1 more source
, 2017 In this study, firstly, a practical and educational geostatistical program (JeoStat) was developed, and then example analysis of porosity parameter distribution, using oilfield data, was presented.
B. Mert, A. Dağ
semanticscholar +1 more source
Bayesian D‐Optimal Designs for Gaussian Process Surrogate Models
Quality and Reliability Engineering International, Volume 42, Issue 2, Page 583-597, March 2026.ABSTRACT Computer experiments often employ space‐filling strategies to create surrogate models with strong predictive performance. The impact of model parameter estimation for Gaussian process surrogates, however, is often overlooked. Obtaining a better initial estimate of the covariance lengthscale parameter, θ$\bm{\theta }$, can greatly improve the ...
Patrick McHugh +2 more
wiley +1 more source
Remote SensingIn low-relief agricultural areas, crop cover makes it challenging to obtain remotely sensed bare soil spectral data for predicting soil texture. Therefore, this study proposed a method for predicting soil texture using crop growth information with ...
Fei Wang +8 more
doaj +1 more source