Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods...

the performance of geostatistical and spatial interpolation techniques for estimation of spatial variability of heavy metals and water quality mapping of groundwater resources in ramiyan district (golestan province- iran) were investigated. 24 spring/well water samples were collected and the concentration of heavy metals (ni, co, pb, cd and cu) was determined using differential pulse polarograp...

The performance of geostatistical and spatial interpolation techniques for estimation of spatial variability of heavy metals and water quality mapping of groundwater resources in Ramiyan district (Golestan province- Iran) were investigated. 24 spring/well water samples were collected and the concentration of heavy metals (Ni, Co, Pb, Cd and Cu) was determined using Differential Pulse Polarograp...

Support Vector Machines (SVM) is a new machine learning approach based on Statistical Learning Theory (Vapnik-Chervonenkis or VC-theory). VCtheory has a solid mathematical background for the dependencies estimation and predictive learning from finite data sets. SVM is based on the Structural Risk Minimisation principle, aiming to minimise both the empirical risk and the complexity of the model,...

In this study we propose a novel method for the estimation of ecological indices describing the habitat suitability of brown trout (Salmo trutta). Traditional hydrological tools are coupled with an innovative regional geostatistical technique, aiming at the prediction of the brown trout habitat suitability index where partial or totally ungauged conditions occur. Several methods for the assessm...

Classical geostatistical methods are powerful tools to study the spatial-temporal structure of stationary processes. Separability is also a common assumption to avoid many of the problems of space and time modeling. This subclass of separable spatial-temporal processes has several advantages, including rapid fitting and simple extensions of many techniques developed and successfully used in tim...

In environmental sciences it is relatively common to encounter certain problems that require specific experimental data treatment by means of spatial estimation methods, for example mapping the spatial distribution of a contaminant concentration exceeding a limit level, an ‘alarm problem’. In such situations the real problem is not directly related to the estimation of the variable at any parti...

This paper compares the results of two models, one hedonic and other geostatistical, when obtaining housing price estimates in Rumiñahui canton, Ecuador. In first model, different parameterizations variables that make up model are used to obtain best predictions. For geostatistical case, predictions composed a trend function depends on certain characteristics spatial error term, which is modele...

Estimation of the metal value in the metallic deposits is one of the important factors to evaluate the deposits in exploration studies andmineral processing. Therefore, one accurate estimator is essential to obtain a fine insight into the accumulation of the ore body. Thereare geostatistical methods for the estimation of the concentration of iron which performance of these models is complexity ...

Estimation of ore reserves is one of the most critical aspects of mining geology. The accurate assessment of the tonnage and grade of run of mine may be the difference between a healthy profitable operation and an expensive early mine closure. The first step in ore reserve estimation is to determine the boundary of ore body or ore/waste contacts. This paper presents a specific mining applicatio...