the aim of this study was to determine the best interpolation method to achieve spatial and quality distribution of springs from point view (tds) and ph in mirdeh watershed nw kurdistan province. field sampling collections was done and 268 springs of total 904 springs of whole watershed selected randomly. electrical conductivity and ph of springs was determined and 168 springs was used as train...

Groundwater drinking water supply surveillance data were accessed to summarize water quality delivered as public and private water supplies in southern Saskatchewan as part of an exposure assessment for epidemiologic analyses of associations between water quality and type 2 diabetes or cardiovascular disease. Arsenic in drinking water has been linked to a variety of chronic diseases and previou...

Keeping the water table at a favorable level is quite significant for a sustainable management of groundwater plans. Various management measures need to know the spatial and temporal behavior of groundwater. Therefore, the measurement of groundwater levels are generally carried out at spatially random locations in the field; whereas, most of the groundwater models requires these measurement at ...

For water levels, generally a non-stationary variable, the technique of universal kriging is applied in preference to ordinary kriging as the interpolation method. Each set of data in every sector can fit different empirical semivariogram models since they have different spatial structures. These models can be classified as circular, spherical, tetraspherical, pentaspherical, exponential, gauss...

Information about soil properties helps the farmers to adopt effective and efficient farming practices, which can increase higher yields with optimum usage of farm resources. An attempt has been made in this paper predict using geospatial kriging approaches. This study mainly focuses on predicting pH different methods. Soil dramatically affects many other processes, such as nitrification denitrifi...

Kriging is a widely employed technique across computer experiments, machine learning and geostatistics. An important challenge for kriging its high costs when dealing with large datasets. This article focuses on class of methods aiming at decreasing this computational burden by aggregating predictors based smaller data subsets. More precisely, it shows that aggregation ignore the covariance bet...

When data from different networks are merged for mapping purpose, biases may appear and lead to unrealistic results. In this paper we define a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data. Multiple bias sources can be treated simultaneously through the notion of bias factors. The associated best linear unbiased estimation and predi...