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Latin hypercube approach to estimate uncertainty in ground water vulnerability

Ground Water

By:
, , ,
DOI: 10.1111/j.1745-6584.2006.00298.x

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Abstract

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Latin hypercube approach to estimate uncertainty in ground water vulnerability
Series title:
Ground Water
DOI:
10.1111/j.1745-6584.2006.00298.x
Volume
45
Issue:
3
Year Published:
2007
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
348
Last page:
361
Number of Pages:
14