A Statistical Analysis of Well Failures in Baltimore County

10.6339/JDS.2009.07(1).423

Xiao-Yin Wang；Kevin W. Koepenick

ANOVA ； Box-Cox transformation ； influence analysis ； logistic regression ； residual ； prediction

Journal of Data Science

7卷1期（2009 / 01 / 01）

111 - 127

英文

A statistical evaluation of the Baltimore County water well database is performed to gain insight on the sustainability of domestic supply wells in crystalline bedrock aquifers over the last 15 years. Variables potentially related to well yield that are considered included well construction, geology, well depth, and static water level. A variety of statistical methods are utilized to assess correlation and significance from a database of approximately 8,500 wells, and a logistic regression model is developed to predict the probability of well failure by geology type. Results of a two-way analysis of variance technique indicate that the average well depth and yield are statistically different among the established geology groups, and between failed and non-failed wells. The static water level is shown to be statistically different among the geology groups but not among failed and non-failed wells. A logistic regression model results that well yield is the most influential variable for predicting well failure. Static water level and well depth was not found to be significant in predicting well failure.

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