We have developed a domain agnostic ordinary kriging algorithm accessible via a standardsbased service-oriented architecture for sensor networks. We exploit the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards. We need on-demand interpolation maps so runtime performance is a major priority. Our sensor data comes from wind in-situ observation stations in an area approximate...

For the six-wheel CRAB rover, this paper presents the torque control with Kriging estimation to improve the terrainability by minimizing wheel slip. In previous works, we presented a torque control approach, effectively improving terrainability by minimizing the variance of the required friction coefficient based on the static model. Using a Kalman filter to estimate the velocity information, a...

The research aim of this paper is to investigate the effectiveness of a new Kriging model which uses Taylor expansion to predict wireless network connectivity. Wireless network connectivity is measured by the strength of emitted signal power from the tower to the point in question. The prediction results are compared with those from the literature where an Ordinary Kriging model and a neural ne...

Understanding spatial variation of potentially toxic trace elements (PTEs) in soil is necessary to identify the proper measures for preventing soil contamination at both operating and abandoned mining areas. Many studies have been conducted worldwide to explore the spatial variation of PTEs and to create soil contamination maps using geostatistical methods. However, they generally depend only o...

This paper examines statistical approaches for interpolating climatic data over large regions, providing a brief introduction to interpolation techniques for climate variables of use in agricultural research, as well as general recommendations for future research to assess interpolation techniques. Three approaches—1) inverse distance weighted averaging (IDWA), 2) thin plate smoothing splines a...

The work deals with the development and use of mixed models (artificial neural networks-ANN and modern geostatistical models) for the analysis of spatially distributed environmental data. When multivariate data have complex non-linear trends or high variability at different scales in the region of study it is proposed to use ANN to model non-linear large scale structures (deterministic trends) ...

Kriging-based exploration strategies often rely on a single Ordinary Kriging model which parametric covariance kernel is selected a priori or on the basis of an initial data set. Since choosing an unadapted kernel can radically harm the results, we wish to reduce the risk of model misspecification. Here we consider the simultaneous use of multiple kernels within Kriging. We give the equations o...

This paper discusses the estimation and plug-in kriging prediction of a non-stationary spatial process assuming a smoothly varying variance function with an additive independent measurement error. A difference-based kernel smoothing estimator of the variance function and a modified likelihood estimator of the measurement error variance are used for parameter estimation. Asymptotic properties of...

The concentrations of As, Hg, Co, Cr and Cd were tested for each soil sample, and their spatial patterns were analyzed by the semivariogram approach of geostatistics and geographical information system technology. Multivariate statistic approaches (principal component analysis and cluster analysis) were used to identify heavy metal sources and their spatial pattern. Principal component analysis...

Method for reducing the number of wild animal monitors is proposed by means of Kriging. Through wild animal route of simulations with 128 by 128 cells, the required number of wild animal monitors is clarified. Then it is found that the number of wild animal monitors can be reduced based on Kriging by using variograms and semi-variograms among the neighboring monitors. Also, it is found that the...