scholarly journals Assessing groundwater quality for irrigation using indicator kriging method

2014 ◽  
Vol 6 (4) ◽  
pp. 371-381 ◽  
Author(s):  
Masoomeh Delbari ◽  
Meysam Amiri ◽  
Masoud Bahraini Motlagh
Keyword(s):  
Groundwater Quality ◽  
Indicator Kriging ◽  
Kriging Method

scholarly journals Review of the Kriging Technique Applications to Groundwater Quality

2021 ◽  
Vol 27 (12) ◽  
pp. 23-32
Author(s):  
Hayat Azawi ◽  
May Samir Saleh
Keyword(s):  
Groundwater Quality ◽  
Quality Indicator ◽  
Ordinary Kriging ◽  
Indicator Kriging ◽  
Disjunctive Kriging ◽  
Kriging Method ◽  
Lognormal Kriging ◽  
Groundwater Contaminants ◽  
Simple Kriging

Kriging, a geostatistical technique, has been used for many years to evaluate groundwater quality. The best estimation data for unsampled points were determined by using this method depending on measured variables for an area. The groundwater contaminants assessment worldwide was found through many kriging methods. The present paper shows a review of the most known methods of kriging that were used in estimating and mapping the groundwater quality. Indicator kriging, simple kriging, cokriging, ordinary kriging, disjunctive kriging and lognormal kriging are the most used techniques. In addition, the concept of the disjunctive kriging method was explained in this work to be easily understood.

scholarly journals Interpolation of groundwater quality parameters with some values below the detection limit

2011 ◽  
Vol 15 (9) ◽  
pp. 2763-2775 ◽  
Author(s):  
A. Bárdossy
Keyword(s):  
Detection Limit ◽  
Maximum Likelihood ◽  
Groundwater Quality ◽  
Likelihood Estimation ◽  
Quality Parameters ◽  
Indicator Kriging ◽  
West Germany ◽  
Marginal Distributions ◽  
Copula Models ◽  
Measuring Device

Abstract. For many environmental variables, measurements cannot deliver exact observation values as their concentration is below the sensitivity of the measuring device (detection limit). These observations provide useful information but cannot be treated in the same manner as the other measurements. In this paper a methodology for the spatial interpolation of these values is described. The method is based on spatial copulas. Here two copula models – the Gaussian and a non-Gaussian v-copula are used. First a mixed maximum likelihood approach is used to estimate the marginal distributions of the parameters. After removal of the marginal distributions the next step is the maximum likelihood estimation of the parameters of the spatial dependence including taking those values below the detection limit into account. Interpolation using copulas yields full conditional distributions for the unobserved sites and can be used to estimate confidence intervals, and provides a good basis for spatial simulation. The methodology is demonstrated on three different groundwater quality parameters, i.e. arsenic, chloride and deethylatrazin, measured at more than 2000 locations in South-West Germany. The chloride values are artificially censored at different levels in order to evaluate the procedures on a complete dataset by progressive decimation. Interpolation results are evaluated using a cross validation approach. The method is compared with ordinary kriging and indicator kriging. The uncertainty measures of the different approaches are also compared.

Improving the Prediction Accuracy of Groundwater Salinity Mapping Using Indicator Kriging Method

2016 ◽  
Vol 142 (7) ◽  
pp. 04016023 ◽  
Author(s):  
Abdelhamid Bradaï ◽  
Abdelkader Douaoui ◽  
Naïma Bettahar ◽  
Ibrahim Yahiaoui
Keyword(s):  
Prediction Accuracy ◽  
Indicator Kriging ◽  
Groundwater Salinity ◽  
Kriging Method

scholarly journals Mapping The Distribution of Iron Content in Nickel Laterite Deposits Using Indicator Kriging and Ordinary Kriging Interpolation Method

PROMINE ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 29-36
Author(s):  
Hendro Purnomo
Keyword(s):  
Root Mean Square ◽  
Iron Content ◽  
Ordinary Kriging ◽  
Interpolation Method ◽  
Indicator Kriging ◽  
Kriging Interpolation ◽  
Mean Square ◽  
Kriging Method ◽  
Nickel Laterite ◽  
Kriging Interpolation Method

Beside containing nickel (Ni), nickel laterite deposits also contain other elements, including iron (Fe) which have varying levels in each layer. In this study, the distribution of Fe content in the limonite layers was carried out using the indicator kriging method to analyze the probability distribution of iron levels and ordinary kriging to analyze the variability of iron levels spatially. Fitting the variogram was undertaken by using spherical, exponential and gaussian models. The selection of the best variogram model was carried out based on the smallest root mean square error (RMSE) value, while the estimation of resource potential was calculated by the polygon extended area method. The results of the interpolation show that the distribution of iron anomaly occupies ± 83,3% of the research area with a potential resource of ±64.522.110 ton of iron. The evaluation of the interpolation results base on the root mean square standardized prediction error (RMSP) indicates that the estimation results of iron content using the ordinary kriging method are underestimated.

Compilation of 3D soil texture dataset applying indicator kriging method, integrating soil- and agrogeological databases

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Zsófia Bakacsi ◽  
László Pásztor ◽  
József Szabó ◽  
László Kuti ◽  
Annamária Laborczi
Keyword(s):  
Soil Texture ◽  
Indicator Kriging ◽  
Kriging Method

Combining natural background levels (NBLs) assessment with indicator kriging analysis to improve groundwater quality data interpretation and management

2016 ◽  
Vol 569-570 ◽  
pp. 569-584 ◽  
Author(s):  
Daniela Ducci ◽  
M. Teresa Condesso de Melo ◽  
Elisabetta Preziosi ◽  
Mariangela Sellerino ◽  
Daniele Parrone ◽  
...  
Keyword(s):  
Groundwater Quality ◽  
Data Interpretation ◽  
Indicator Kriging ◽  
Quality Data ◽  
Natural Background ◽  
Background Levels ◽  
Kriging Analysis
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