Efficient soil management practices depend on the spatial distribution of soil properties which varies significantly even within the same field. Considering that it is impossible for any monitoring technique to provide spatially continuous data, spatial interpolation plays an indispensable role in estimating the missing values where no actual value was measured. The objective of this study was ...

Machine Learning (ML) algorithms have been used as an alternative to conventional and geostatistical methods in digital mapping of soil attributes. An advantage ML is their flexibility use various layers information covariates. However, come many variations that can make application by end users difficult. To fill this gap, a Smart-Map plugin, which complements Geographic Information System QGI...

1. Abstract Over three decades, metamodeling has been widely applied to design optimization problems to build a surrogate model of computation-intensive engineering models. The Kriging method has gained significant interests for developing the surrogate model. However, traditional Kriging methods, including the ordinary Kriging and the universal Kriging, use fixed polynomials basis functions to...

In this paper, we implement and compare the accuracy of ordinary kriging, lognormal ordinary kriging, inverse distance weighting (IDW) and splines for interpolating seasonally stable soil properties (pH, electric conductivity and organic matter) that have been demonstrated to affect yield production. The choice of the exponent value for IDW and splines as well as the number of the closest neigh...

Accurate rainfall data are essential for environmental applications in the actual assessment of geographical distribution rainfall. Interpolation methods usually applied to monitor spatial data. There many interpolation methods, but none them can achieve all cases best results. In this study, three different were investigated with regard their suitability producing a distribution. Rainfall from...

Spatial interpolation methods usually differ in their underlying mathematical concepts. Each has inherent advantages and disadvantages, choosing a method should be based on the type of data to analyzed. This paper, therefore, compares evaluates performances well-established techniques that can used estimate monthly rainfall Thailand. The approaches analyzed include inverse distance weighting (I...

Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability resilience often poorly recognized. In current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed ap...

In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them...

Variograms are used to describe the spatial variability of environmental variables. In this study, the parameters that characterize the variogram are obtained from a variogram in a different but comparably polluted area. A procedure is presented for improving the variogram modelling when data become available from the area of interest. Interpolation is carried out by means of a Bayesian form of...