Statistical modelling of groundwater contamination monitoring data: A comparison of spatial and spatiotemporal methods

McLean, Marnie Isla; Evers, Ludger; Bowman, Adrian; Bonte, Matthijs; Jones, Wayne

doi:10.1016/j.scitotenv.2018.10.231

Cited by 27 publications

(16 citation statements)

References 23 publications

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“…The assumption of this method is the distance among sample data showing the important geographical correlation on the interpolation result [29]. IDW provide more accurate interpolation result than the kriging [30].…”

Section: Description Of Protocolmentioning

confidence: 99%

Health risk assessment of nitrate in groundwater resources of Iranshahr using Monte Carlo simulation and geographic information system (GIS)

et al. 2019

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Section: Description Of Protocolmentioning

confidence: 99%

Health risk assessment of nitrate in groundwater resources of Iranshahr using Monte Carlo simulation and geographic information system (GIS)

et al. 2019

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“…Meanwhile, the applications of PCA included the predication of a dynamic potentiometric head of the location, recognition of contaminants, and calculation of monitoring site loadings. In another study conducted by McLean, Evers, Bowman, Bonte, and Jones (2019), the spatiotemporal statistic model was compared with spatial kriging and spatial splines in the simulation of monitoring groundwater polluted with plumes. This study showed that spatiotemporal model obtained a smoother and more accurate estimation than other tools and indicated a crucial improvement of the technology on monitoring groundwater.…”

Section: Groundwater Monitoringmentioning

confidence: 99%

Groundwater quality

Motlagh

Yang

Saba

2020

Water Environment Research

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Groundwater is a vital component of water supply for residential, industrial, and agricultural purposes. However, many groundwater basins are being used unsustainably and groundwater contamination is a growing water quality problem. Although anthropogenic activities and natural processes have been increasing the contamination in this valuable water resource, several remediation techniques have been developed in the last few decades to reduce these contamination levels. This review paper focuses on the recent studies developed on groundwater pollutions, remediation practices, and groundwater quality management.

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“…Spatial smoothing plays an important role in a broad range of applications, including the assessment of feature significance [12] and seafloor classification in geostatistics [13], monitoring of groundwater contaminant plumes [14], image processing [11,15], the calorific value distributions in coal facies [16], and analysis of traffic accidents [17,18] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing

Duncan

Mengersen

2020

PLoS ONE

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BackgroundMany methods of spatial smoothing have been developed, for both point data as well as areal data. In Bayesian spatial models, this is achieved by purposefully designed prior(s) or smoothing functions which smooth estimates towards a local or global mean. Smoothing is important for several reasons, not least of all because it increases predictive robustness and reduces uncertainty of the estimates. Despite the benefits of smoothing, this attribute is all but ignored when it comes to model selection. Traditional goodness-of-fit measures focus on model fit and model parsimony, but neglect "goodness-of-smoothing", and are therefore not necessarily good indicators of model performance. Comparing spatial models while taking into account the degree of spatial smoothing is not straightforward because smoothing and model fit can be viewed as opposing goals. Over-and under-smoothing of spatial data are genuine concerns, but have received very little attention in the literature. MethodsThis paper demonstrates the problem with spatial model selection based solely on goodness-of-fit by proposing several methods for quantifying the degree of smoothing. Several commonly used spatial models are fit to real data, and subsequently compared using the goodness-of-fit and goodness-of-smoothing statistics. ResultsThe proposed goodness-of-smoothing statistics show substantial agreement in the task of model selection, and tend to avoid models that over-or under-smooth. Conversely, the traditional goodness-of-fit criteria often don't agree, and can lead to poor model choice. In particular, the well-known deviance information criterion tended to select under-smoothed models. ConclusionsSome of the goodness-of-smoothing methods may be improved with modifications and better guidelines for their interpretation. However, these proposed goodness-of-smoothing

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Statistical modelling of groundwater contamination monitoring data: A comparison of spatial and spatiotemporal methods

Cited by 27 publications

References 23 publications

Health risk assessment of nitrate in groundwater resources of Iranshahr using Monte Carlo simulation and geographic information system (GIS)

Health risk assessment of nitrate in groundwater resources of Iranshahr using Monte Carlo simulation and geographic information system (GIS)

Groundwater quality

Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing

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