Results 201 to 210 of about 2,909,867 (244)

Universal Kriging and Cokriging as a Regression Procedure

Biometrics, 1991
Prediction of a property on the basis of a set of point measurements in a region is required if a map of this property for the region is to be made. Of the spatial interpolation and prediction techniques, kriging is optimal among all linear procedures, as it is unbiased and has minimal variance of the prediction error. In cokriging, which has this same
Stein, A., Corsten, L.C.A.
openaire   +3 more sources

Comparing Ordinary Kriging and Regression Kriging for Soil Properties in Contrasting Landscapes

Pedosphere, 2010
Abstract The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size, spatial structure, and auxiliary variables (terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes (agricultural vs. forested).
Q. ZHU, H.S. LIN
openaire   +1 more source

Regression Kriging Analysis for Longitudinal Dispersion Coefficient

Water Resources Management, 2013
Prediction of longitudinal dispersion coefficient (LDC) is still a novel topic for both environmental and water sciences due to its practical importance. In this study, the appraisal of LDC is considered as a spatial modelling problem and the analyses are carried out by regression kriging.
Bulent Tutmez, Mehmet Yuceer
openaire   +1 more source

Generalized Kriging Model for Interpolation and Regression

Transactions of the Korean Society of Mechanical Engineers A, 2005
Kriging model is widely used as design analysis and computer experiment (DACE) model in the field of engineering design to accomplish computationally feasible design optimization. In general, kriging model has been applied to many engineering applications as an interpolation model because it is usually constructed from deterministic simulation ...
Jae Jun Jung, Tae Hee Lee
openaire   +1 more source

Spatial Downscaling of Nighttime Land Surface Temperature Based on Geographically Neural Network Weighted Regression Kriging

Remote Sensing
Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data.
Jihan Wang, Nan Zhang, Laifu Zhang, Haoyu Jing, Yiming Yan, Sensen Wu, Renyi Liu   +6 more
semanticscholar   +1 more source

Can regression determination, nugget-to-sill ratio and sampling spacing determine relative performance of regression kriging over ordinary kriging?

CATENA, 2019
Abstract Regression kriging (RK), a popular digital soil mapping method which combines regression and ordinary kriging (OK) to predict, did not always perform better than OK purely. A lot of studies tried to establish approaches for determining relative improvement (RI) of RK over OK in terms of two of three factors, i.e., regression determination ...
Xiao-Lin Sun, Qian Yang, Hui-Li Wang, Yun-Jin Wu   +3 more
openaire   +1 more source

Improving prediction of soil heavy metal(loid) concentration by developing a combined Co-kriging and geographically and temporally weighted regression (GTWR) model.

Journal of Hazardous Materials
The study of heavy metal(loid) (HM) contamination in soil using extensive data obtained from published literature is an economical and convenient method.
Huijuan Wang, Menglu Zhao, Xinmiao Huang, Xiaoyong Song, Bo-feng Cai, Rui Tang, Jiaxun Sun, Zilin Han, Jing Yang, Yafeng Liu, Zhengqiu Fan   +10 more
semanticscholar   +1 more source

Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles

IEEE transactions on intelligent transportation systems (Print), 2019
Geostatistical methods have been widely used for spatial prediction and the assessment of traffic issues. Most previous studies use point-based interpolation, but they ignore the critical information of the road segment itself.
Yongze Song, Xiangyu Wang, G. Wright, D. Thatcher, Peng Wu, Pascal Felix   +5 more
semanticscholar   +1 more source

Regression-kriging for characterizing soils with remotesensing data

Frontiers of Earth Science, 2011
In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually violates a basic assumption of regression: sample independence.
Yufeng Ge, J. Alex Thomasson, Ruixiu Sui, James Wooten   +3 more
openaire   +1 more source

High-resolution satellite image fusion using regression kriging

International Journal of Remote Sensing, 2010
Image fusion is an important component of digital image processing and quantitative image analysis. Image fusion is the technique of integrating and merging information from different remote sensors to achieve refined or improved data. A number of fusion algorithms have been developed in the past two decades, and most of these methods are efficient for
Qingmin Meng, Bruce Borders, Marguerite Madden   +2 more
openaire   +1 more source