The Experts below are selected from a list of 46860 Experts worldwide ranked by ideXlab platform
David J Chittleborough - One of the best experts on this subject based on the ideXlab platform.
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further results on prediction of soil properties from terrain attributes heterotopic coKriging and regression Kriging
Geoderma, 1995Co-Authors: Alex B Mcbratney, Inakwu O A Odeh, David J ChittleboroughAbstract:Several methods involving spatial prediction of soil properties from landform attributes are compared using carefully designed validation procedures. The methods, tested against ordinary Kriging and universal Kriging of the target variables, include multi-linear regression, isotopic coKriging, heterotopic coKriging and regression-Kriging models A, B and C. Prediction performance by ordinary Kriging and universal Kriging was comparatively poor as the methods do not use covariation of the predictor variable with terrain attributes. Heterotopic coKriging outperformed isotopic coKriging because the former utilised more of the local information from the covariables. The combined regression-Kriging methods generally performed well. Both the regression-Kriging model C and heterotopic coKriging performed well when soil variables were predicted into a relatively finer gridded digital elevation model (DEM) and when all the local information was utilised. Regression-Kriging model C generally performed best and is, perhaps, more flexible than heterotopic coKriging. Potential for further research and developments rests in improving the regression part of model C.
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spatial prediction of soil properties from landform attributes derived from a digital elevation model
Geoderma, 1994Co-Authors: I O A Odeha, Alex B Mcbratney, David J ChittleboroughAbstract:Abstract Digital elevation models (DEMs) provide a good way of deriving landform attributes that may be used for soil prediction. The geostatistical techniques of Kriging and coKriging are increasingly being applied to predicting soil properties. Whereas ordinary Kriging (and universal Kriging) utilise spatial correlation to determine the coefficients of the linear predictor, coKriging involves both inter-variable correlation and spatial covariation among variables. Multi-linear regression modelling also offers an alternative to predicting a soil variable by means of covariation. The performance of predicting four soil variables by these methods and two regression-Kriging models are compared. The precision and bias of prediction of the six methods were dependent on the soil variable predicted. The mean error of prediction indicates reasonably small bias of prediction for all the soil variables by almost all of the methods. With the exception of topsoil gravel, for which multi-linear regression performed best, the root mean square error showed the two regression-Kriging procedures to be best. Further analysis based on the mean ranks of performance by the methods confirmed this. All the Kriging methods involving covariables (landform attributes) have a more smoothing effect on the predicted values, thus minimising the influence of outliers on prediction performance. Both the methods of regression-Kriging show promise for predicting sparsely located soil properties from dense observations of landform attributes derived from the DEM. Histograms of subsoil clay residuals show outliers in the data set. These outliers are more evident in multi-linear regression, ordinary Kriging and universal Kriging than regression-Kriging. There was a clear advantage in using the regression-Kriging methods on those variables which had a small correlation with the landform attributes: root mean square errors for all the soil variables are much smaller than those resulting from any of the multi-linear regression, ordinary Kriging, universal Kriging or coKriging methods.
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spatial prediction of soil properties from landform attributes derived from a digital elevation model
Geoderma, 1994Co-Authors: I O A Odeha, Alex B Mcbratney, David J ChittleboroughAbstract:Abstract Digital elevation models (DEMs) provide a good way of deriving landform attributes that may be used for soil prediction. The geostatistical techniques of Kriging and coKriging are increasingly being applied to predicting soil properties. Whereas ordinary Kriging (and universal Kriging) utilise spatial correlation to determine the coefficients of the linear predictor, coKriging involves both inter-variable correlation and spatial covariation among variables. Multi-linear regression modelling also offers an alternative to predicting a soil variable by means of covariation. The performance of predicting four soil variables by these methods and two regression-Kriging models are compared. The precision and bias of prediction of the six methods were dependent on the soil variable predicted. The mean error of prediction indicates reasonably small bias of prediction for all the soil variables by almost all of the methods. With the exception of topsoil gravel, for which multi-linear regression performed best, the root mean square error showed the two regression-Kriging procedures to be best. Further analysis based on the mean ranks of performance by the methods confirmed this. All the Kriging methods involving covariables (landform attributes) have a more smoothing effect on the predicted values, thus minimising the influence of outliers on prediction performance. Both the methods of regression-Kriging show promise for predicting sparsely located soil properties from dense observations of landform attributes derived from the DEM. Histograms of subsoil clay residuals show outliers in the data set. These outliers are more evident in multi-linear regression, ordinary Kriging and universal Kriging than regression-Kriging. There was a clear advantage in using the regression-Kriging methods on those variables which had a small correlation with the landform attributes: root mean square errors for all the soil variables are much smaller than those resulting from any of the multi-linear regression, ordinary Kriging, universal Kriging or coKriging methods.
Alex B Mcbratney - One of the best experts on this subject based on the ideXlab platform.
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further results on prediction of soil properties from terrain attributes heterotopic coKriging and regression Kriging
Geoderma, 1995Co-Authors: Alex B Mcbratney, Inakwu O A Odeh, David J ChittleboroughAbstract:Several methods involving spatial prediction of soil properties from landform attributes are compared using carefully designed validation procedures. The methods, tested against ordinary Kriging and universal Kriging of the target variables, include multi-linear regression, isotopic coKriging, heterotopic coKriging and regression-Kriging models A, B and C. Prediction performance by ordinary Kriging and universal Kriging was comparatively poor as the methods do not use covariation of the predictor variable with terrain attributes. Heterotopic coKriging outperformed isotopic coKriging because the former utilised more of the local information from the covariables. The combined regression-Kriging methods generally performed well. Both the regression-Kriging model C and heterotopic coKriging performed well when soil variables were predicted into a relatively finer gridded digital elevation model (DEM) and when all the local information was utilised. Regression-Kriging model C generally performed best and is, perhaps, more flexible than heterotopic coKriging. Potential for further research and developments rests in improving the regression part of model C.
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spatial prediction of soil properties from landform attributes derived from a digital elevation model
Geoderma, 1994Co-Authors: I O A Odeha, Alex B Mcbratney, David J ChittleboroughAbstract:Abstract Digital elevation models (DEMs) provide a good way of deriving landform attributes that may be used for soil prediction. The geostatistical techniques of Kriging and coKriging are increasingly being applied to predicting soil properties. Whereas ordinary Kriging (and universal Kriging) utilise spatial correlation to determine the coefficients of the linear predictor, coKriging involves both inter-variable correlation and spatial covariation among variables. Multi-linear regression modelling also offers an alternative to predicting a soil variable by means of covariation. The performance of predicting four soil variables by these methods and two regression-Kriging models are compared. The precision and bias of prediction of the six methods were dependent on the soil variable predicted. The mean error of prediction indicates reasonably small bias of prediction for all the soil variables by almost all of the methods. With the exception of topsoil gravel, for which multi-linear regression performed best, the root mean square error showed the two regression-Kriging procedures to be best. Further analysis based on the mean ranks of performance by the methods confirmed this. All the Kriging methods involving covariables (landform attributes) have a more smoothing effect on the predicted values, thus minimising the influence of outliers on prediction performance. Both the methods of regression-Kriging show promise for predicting sparsely located soil properties from dense observations of landform attributes derived from the DEM. Histograms of subsoil clay residuals show outliers in the data set. These outliers are more evident in multi-linear regression, ordinary Kriging and universal Kriging than regression-Kriging. There was a clear advantage in using the regression-Kriging methods on those variables which had a small correlation with the landform attributes: root mean square errors for all the soil variables are much smaller than those resulting from any of the multi-linear regression, ordinary Kriging, universal Kriging or coKriging methods.
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spatial prediction of soil properties from landform attributes derived from a digital elevation model
Geoderma, 1994Co-Authors: I O A Odeha, Alex B Mcbratney, David J ChittleboroughAbstract:Abstract Digital elevation models (DEMs) provide a good way of deriving landform attributes that may be used for soil prediction. The geostatistical techniques of Kriging and coKriging are increasingly being applied to predicting soil properties. Whereas ordinary Kriging (and universal Kriging) utilise spatial correlation to determine the coefficients of the linear predictor, coKriging involves both inter-variable correlation and spatial covariation among variables. Multi-linear regression modelling also offers an alternative to predicting a soil variable by means of covariation. The performance of predicting four soil variables by these methods and two regression-Kriging models are compared. The precision and bias of prediction of the six methods were dependent on the soil variable predicted. The mean error of prediction indicates reasonably small bias of prediction for all the soil variables by almost all of the methods. With the exception of topsoil gravel, for which multi-linear regression performed best, the root mean square error showed the two regression-Kriging procedures to be best. Further analysis based on the mean ranks of performance by the methods confirmed this. All the Kriging methods involving covariables (landform attributes) have a more smoothing effect on the predicted values, thus minimising the influence of outliers on prediction performance. Both the methods of regression-Kriging show promise for predicting sparsely located soil properties from dense observations of landform attributes derived from the DEM. Histograms of subsoil clay residuals show outliers in the data set. These outliers are more evident in multi-linear regression, ordinary Kriging and universal Kriging than regression-Kriging. There was a clear advantage in using the regression-Kriging methods on those variables which had a small correlation with the landform attributes: root mean square errors for all the soil variables are much smaller than those resulting from any of the multi-linear regression, ordinary Kriging, universal Kriging or coKriging methods.
Jack P C Kleijnen - One of the best experts on this subject based on the ideXlab platform.
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Kriging metamodeling in simulation a review
European Journal of Operational Research, 2009Co-Authors: Jack P C KleijnenAbstract:This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas—contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampling, it reviews sequentialized and customized designs for sensitivity analysis and optimization. It ends with topics for future research.
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Kriging metamodeling in simulation a review
Other publications TiSEM, 2007Co-Authors: Jack P C KleijnenAbstract:This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas--contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampling, it reviews sequentialized and customized designs for sensitivity analysis and optimization. It ends with topics for future research. (This abstract was borrowed from another version of this item.)
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Kriging interpolation in simulation a survey
Winter Simulation Conference, 2004Co-Authors: W C M Van Beers, Jack P C KleijnenAbstract:Many simulation experiments require much computer time, so they necessitate interpolation for sensitivity analysis and optimization. The interpolating functions are 'metamodels' (or 'response surfaces') of the underlying simulation models. Classic methods combine low-order polynomial regression analysis with fractional factorial designs. Modern Kriging provides 'exact' interpolation, i.e., predicted output values at inputs already observed equal the simulated output values. Such interpolation is attractive in deterministic simulation, and is often applied in computer aided engineering. In discrete-event simulation, however, Kriging has just started. Methodologically, a Kriging metamodel covers the whole experimental area; i.e., it is global (not local). Kriging often gives better global predictions than regression analysis. Technically, Kriging gives more weight to 'neighboring' observations. To estimate the Kriging metamodel, space filling designs are used; for example, latin hypercube sampling (LHS). This paper also presents novel, customized (application driven) sequential designs based on cross-validation and bootstrapping.
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the correct Kriging variance estimated by bootstrapping
Social Science Research Network, 2004Co-Authors: Dick Den Hertog, Jack P C Kleijnen, A Y D SiemAbstract:The classic Kriging variance formula is widely used in geostatistics and in the design and analysis of computer experiments. This paper proves that this formula is wrong. Furthermore, it shows that the formula underestimates the Kriging variance in expectation. The paper develops parametric bootstrapping to estimate the Kriging variance. The new method is tested on several artificial examples and a real-life case study. These results demonstrate that the classic formula underestimates the true Kriging variance.
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the correct Kriging variance estimated by bootstrapping
Other publications TiSEM, 2004Co-Authors: Dick Den Hertog, Jack P C Kleijnen, A Y D SiemAbstract:The classic Kriging variance formula is widely used in geostatistics and in the design and analysis of computer experiments. This paper proves that this formula is wrong. Furthermore, it shows that the formula underestimates the Kriging variance in expectation. The paper develops parametric bootstrapping to estimate the Kriging variance. The new method is tested on several artificial examples and a real-life case study. These results demonstrate that the classic formula underestimates the true Kriging variance. (This abstract was borrowed from another version of this item.)
G. Blöschl - One of the best experts on this subject based on the ideXlab platform.
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spatial prediction of stream temperatures using top Kriging with an external drift
Environmental Modeling & Assessment, 2013Co-Authors: Gregor Laaha, J. O. Skøien, Franz Nobilis, G. BlöschlAbstract:Top-Kriging is a method for estimating stream flow and stream flow-related variables on a river network. Top-Kriging treats these variables as emerging from a two-dimensional spatially continuous process in the landscape. The top-Kriging weights are estimated by a family of variogram models (regularisations) for different catchment areas (Kriging support), which accounts for the different scales and the nested nature of the catchments. This assures that Kriging weights are distributed to both hydrologically connected and unconnected sites of the stream network according to the data situation: top-Kriging gives most weight to close-by sites at the same river system, but when the next hydrologically connected site is far away, more weight is given to a close-by site at an adjacent river system. The distribution of weights is in contrast to ordinary Kriging and stream distance-based Kriging which does not account for both spatial proximity and network connectivity. We extend the top-Kriging method by incorporating an external drift function to account for the deterministic patterns of the spatial variable. We test the method for a comprehensive Austrian stream temperature dataset. The drift is modelled by exponential regression with catchment altitude. Top-Kriging is then applied to the regression residuals. The variogram used in top-Kriging is fitted by a semiautomatic optimisation procedure. A leave-one-out cross-validation analysis shows that the model performs well for the study domain. The residual mean squared error (cross-validation) decreases by 20 % when using top-Kriging in addition to the regression model. For regions where the observed stream temperatures deviate from the expected value of the drift model, top-Kriging corrects these regional biases. By exploiting the topological information of the stream network, top-Kriging is able to improve the local adjustment of the drift model for the main streams and the tributaries.
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Top-Kriging - geostatistics on stream networks
Hydrology and Earth System Sciences Discussions, 2006Co-Authors: J. O. Skøien, R. Merz, G. BlöschlAbstract:We present Top-Kriging, or topological Kriging, as a method for estimating streamflow-related variables in ungauged catchments. It takes both the area and the nested nature of catchments into account. The main appeal of the method is that it is a best linear unbiased estimator (BLUE) adapted for the case of stream networks without any additional assumptions. The concept is built on the work of Sauquet et al. (2000) and extends it in a number of ways. We test the method for the case of the specific 100-year flood for two Austrian regions. The method provides more plausible and, indeed, more accurate estimates than Ordinary Kriging. For the variable of interest, Top-Kriging also provides estimates of the uncertainty. On the main stream the estimated uncertainties are smallest and they gradually increase as one moves towards the headwaters. The method as presented here is able to exploit the information contained in short records by accounting for the uncertainty of each gauge. We suggest that Top-Kriging can be used for spatially interpolating a range of streamflow-related variables including mean annual discharge, flood characteristics, low flow characteristics, concentrations, turbidity and stream temperature.
Andrey Shichkin - One of the best experts on this subject based on the ideXlab platform.
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high variation topsoil pollution forecasting in the russian subarctic using artificial neural networks combined with residual Kriging
Applied Geochemistry, 2017Co-Authors: Dmitry Tarasov, A G Buevich, A P Sergeev, Andrey ShichkinAbstract:The work deals with the application of artificial neural networks combined with residual Kriging (ANNRK) to the spatial prediction of the anomaly distributed chemical element Chromium (Cr). In the work, we examined and compared two neural networks: generalized regression neural network (GRNN) and multi-layer perceptron (MLP) as well as two combined techniques: generalized regression neural network residual Kriging (GRNNRK) and multi-layer perceptron residual Kriging (MLPRK). The case study is based on the real measurements of surface contamination by Cr in subarctic city Novy Urengoy, Russia. The networks structures have been chosen during a computer simulation based on a minimization of the root mean square error (RMSE). Different prediction approaches are compared by a Spearman's rank correlation coefficient, the mean absolute error (MAE), and RMSE. MLPRK and GRNNRK show the best predictive accuracy comparing to Kriging and even to MLP and GRNN, that is hybrid models are more accurate than solo models. The most significant improvement in RMSE (15.5% compared to Kriging) is observed in the MLPRK model. The proposed hybrid approach improves the high variation topsoil spatial pollution forecasting, which might be utilized in the environmental modeling.
