The assessment of contaminated land often requires the collection and analysis soil samples discrete or continuous modeling contamination. involves interpolating analyte concentrations between sampled positions. contamination can be expressed either as a map showing spatial distribution across site, frequency distribution, both which compared with threshold. characterization is affected by rand...

The assessment of groundwater irrigation using robust tools is essential for the sustenance agro-environment in arid and semi-arid regions. This study presents a reliable method consisting combination multivariate analysis geostatistical modeling to assess resources Western Middle Cheliff (Algeria). For this goal, mean data from 87 wells collected during April July 2017 were used. hierarchical ...

This paper presented the levels of PM2.5 and PM10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM2.5 and PM10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF)...

Information about the distribution and abundance of the habitat-forming sessile organisms in marine ecosystems is of great importance for conservation and natural resource managers. Spatial interpolation methodologies can be useful to generate this information from in situ sampling points, especially in circumstances where remote sensing methodologies cannot be applied due to small-scale spatia...

In this paper, results from three different interpolation techniques based on Geostatistics (ordinary kriging, kriging with external drift and conditional simulation) and one deterministic method (inverse distances) for mapping total monthly rainfall are compared. The study data set comprised total monthly rainfall from 1998 till 2001 corresponding to a maximum of 121 meteorological stations ir...

This paper presents three multivariate geostatistical algorithms for incorporating a digital elevation model into the spatial prediction of rainfall: simple kriging with varying local means; kriging with an external drift; and colocated cokriging. The techniques are illustrated using annual and monthly rainfall observations measured at 36 climatic stations in a 5000 km region of Portugal. Cross...

The lower Athabasca River watershed is one of the most important regions for Alberta and elsewhere due to fact that it counts for the third largest oil reserve in the world. In order to support the oil and gas extraction, Athabasca River provides most of the required water supply. Thus, it is critical to understand the characteristics of the river and its watershed in order to develop sustainab...

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...

The dispersion of air pollutants and the spatial representation meteorological variables are subject to complex atmospheric local parameters. To reduce impact particulate matter (PM2.5) on human health, it is great significance know its concentration at high resolution. In order monitor effects an exposed population, geostatistical analysis offers potential obtain high-quality mapping PM2.5 var...

Real-time assessment of the ambient air quality has gained an increased interest in recent years. To give support to this evolution, the statistical air pollution interpolation model RIO is developed. Due to the very low computational cost this interpolation model is an efficient tool for an environment agency when performing real-time air quality assessments. Beside this, a reliable interpolat...