Evaluation and optimisation of groundwater observation networks using the Kriging methodology

Theodossiou, Nicolaos; Latinopoulos, P.

doi:10.1016/j.envsoft.2005.05.001

Cited by 148 publications

(72 citation statements)

References 9 publications

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“…The verification procedure does not aim to prove the correctness of the model but to ensure the absence of systematic errors [11,18]. This was carried out by making variographic analysis of all the initial parameters.…”

Section: Resultsmentioning

confidence: 99%

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Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India

Nabi

Gallardo²,

Ahmed

2011

Sustainability

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Abstract:Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contamination makes imperative the implementation of a systematic groundwater-quality monitoring program in the region. Nonetheless, limited funding and resources emphasize the need to achieve a representative but cost-effective sampling strategy. In this context, field observations were combined with a geostatistical analysis to define an optimized monitoring network able to provide sufficient and non-redundant information on key hydrochemical parameters. A factor analysis was used to evaluate the interrelationship among variables, and permitted to reduce the original dataset into a new configuration of monitoring points still able to capture the spatial variability in the groundwater quality of the basin. The approach is useful to maximize data collection and contributes to better manage the allocation of resources under budget constrains.

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Section: Resultsmentioning

confidence: 99%

“…On the other hand, kriging is a method for linear optimum unbiased interpolation with a minimum mean interpolation error [11]. Kriging is only a technique among many others for interpolation of a variable.…”

Section: Theoretical Considerationsmentioning

confidence: 99%

Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India

Nabi

Gallardo²,

Ahmed

2011

Sustainability

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“…For example, Theodossiou and Latinopoulos [1] employed Kriging to interpolate the groundwater level in the Anthemountas basin of northern Greece, and estimated the accuracy of interpolated values using cross-validation; Ahmadi and Sedghamiz [2] applied Kriging and co-Kriging to map the groundwater depth; Sun et al [3] compared inverse distance weighting, radial basis function, and Kriging methods to estimate the groundwater level in a real case study, and concluded that Kriging is the optimal method for the interpolation of groundwater level in their study. In all of the applications mentioned above, two-point statistics (i.e., covariance) are used to quantify the spatial correlations, which may be inappropriate for the interpolation of the groundwater level data for complex formations such as fluvial deposition and fractured aquifers.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Level Mapping Using Multiple-Point Geostatistics

Huang

2016

Water

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Abstract:Methods based on two-point geostatistics have been routinely used to interpolate random variables such as groundwater level and concentration and to estimate their values at un-sampled locations. These methods use the observed data to analyze spatial two-point correlations and ignore the higher order moments that may play a key role in the characterization of complex patterns. In this work, a multiple-point geostatistics method is applied to interpolate groundwater level data. To do this, the ensemble simulated groundwater level maps after modeling full physics are used as training images to derive the multiple correlations. An example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of groundwater level data in a complex geological formation.

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“…In another research, Theodossiou and latinopoulos (2007) used kriging to estimate the level of groundwater in Anthemountas Basin of northern Greece. hooshmand et al, (2011) used kriging and co-kriging to estimate the absorption of sodium and chloride in groundwater of Bukan (north-west of Iran).…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

et al. 2015

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Artificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m -1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m -1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.

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Evaluation and optimisation of groundwater observation networks using the Kriging methodology

Cited by 148 publications

References 9 publications

Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India

Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India

Groundwater Level Mapping Using Multiple-Point Geostatistics

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

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