Date of Award:

8-2012

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Mac McKee

Committee

Mac McKee

Committee

Jagath Kaluaratchchi

Committee

David Stevens

Committee

David Rosenberg

Committee

Arthur Caplan

Abstract

Due to variations in rainfall and limited surface water resources, groundwater is considered the main source for freshwater in many places throughout the word. But this precious resource is being jeopardized by pollution from human activities such as: industry, agriculture, and untreated wastewater, which highlight the need for efficient groundwater resources management. To be efficient, groundwater resources management requires efficient access to reliable information that can be acquired through monitoring. On the other hand, the complicated nature of groundwater aquifers and the uncertainties in the data and the models used to understand the aquifer and its behavior require more powerful and sophisticated tools to handle monitoring problems. Another problem is the limited resources for monitoring, which requires cost-efficient designs.

This research introduces a methodology for groundwater quality monitoring network design that utilizes state-of-the-art learning machines that have been developed from the general area of statistical learning theory. The methodology takes into account uncertainties in aquifer properties, pollution transport processes, and climate. To check the feasibility of the network design, the research introduces a methodology to estimate the value of information (VOI) provided by the network using a decision tree model. Finally, the research presents the results of a survey administered in the study area to determine whether the implementation of the monitoring network design could be supported.

Applying these methodologies on the Eocene Aquifer, Palestine indicates that statistical learning machines can be most effectively used to design a groundwater quality monitoring network in real-life aquifers. On the other hand, VOI analysis indicates that for the value of monitoring to exceed the cost of monitoring, more work is needed to improve the accuracy of the network and to increase people’s awareness of the pollution problem and the available alternatives.

Checksum

7557919ebaf5977be4bbd06725d91941

Comments

This work made publicly available electronically on September 20, 2012.

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