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Regression Kriging Analysis for Longitudinal Dispersion Coefficient
Water Resources Management, 2013Prediction 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
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Nonparametric Regression, Kriging and Process Optimization
1995Thin plate splines and kriging models are proposed as methods for approximating unknown response functions in the context of process optimization. Connections between the methods are discussed and implementation of the models using S-PLUS is described.
M. O’Connell +3 more
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Ying yong sheng tai xue bao = The journal of applied ecology, 2016
Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control.
Shun-hua, Yang +3 more
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Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control.
Shun-hua, Yang +3 more
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Optimally Weighted Cluster Kriging for Big Data Regression
2015In business and academia we are continuously trying to model and analyze complex processes in order to gain insight and optimize. One of the most popular modeling algorithms is Kriging, or Gaussian Processes. A major bottleneck with Kriging is the amount of processing time of at least \(O(n^3)\) and memory required \(O(n^2)\) when applying this ...
Bas van Stein +4 more
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Linear Regression and Simple Kriging
1998Linear regression is presented in the case of two variables and then extended to the multivariate case. Simple kriging is a transposition of multiple regression in a spatial context. The algebraic problems generated by missing values in the multivariate case serve as a motivation for introducing a covariance function in the spatial case.
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2007 Minneapolis, Minnesota, June 17-20, 2007, 2007
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 makes the regression model suboptimal. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing ...
null Yufeng Ge +3 more
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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 makes the regression model suboptimal. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing ...
null Yufeng Ge +3 more
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Regression kriging as a workhorse in the digital soil mapper's toolbox
Geoderma, 2018Abstract Appropriate scale, justifiably reliable, categorical and continuous spatial soil information is urgently needed to address environmental problems and ensure sustainability of ecosystem services at local, regional and global scales. Regression Kriging (RK) is one of the most popular, practical and robust hybrid spatial interpolation ...
H. Keskin, S. Grunwald
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A regression-kriging model for estimation of rainfall in the Laohahe basin
SPIE Proceedings, 2009ABSTRACT This paper presents a multivariate geostatistical algorithm called regression-kriging (RK) for predicting the spatial distribution of rainfall by incorporating five topographic/geographic factors of latitude, longitude, altitude, slope and aspect.
Hong Wang, Li L. Ren, Gao H. Liu
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Population‐density estimation using regression and area‐to‐point residual kriging
International Journal of Geographical Information Science, 2008Census population data are associated with several analytical and cartographic problems. Regression models using remote-sensing covariates have been examined to estimate urban population density, but the performance may not be satisfactory. This paper describes a kriging-based areal interpolation method, namely area-to-point residual kriging, which can
X. H. Liu +2 more
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Estimating Soil Organic Matter Content by Regression Kriging
2010In Mediterranean countries soil organic matter (SOM) depletion is a key factor in land degradation. Here, climate (temperate winter/dry summer) and water scarcity give rise to faster mineralization rates and lower accumulation intensities, particularly in association with intensive and non-conservative agronomic practices.
A. Marchetti +4 more
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