Contaminant remediation decision analysis using information gap theory

TL;DR

This paper applies information gap theory to support decision making in contaminant remediation under severe uncertainty, focusing on determining the optimal contaminant mass removal despite limited flux information.

Contribution

It introduces an info-gap based decision analysis framework for contaminant remediation, explicitly modeling uncertainty due to lack of flux data and deriving robustness and opportuneness functions.

Findings

Robustness decreases as time since remediation increases.

Opportuneness varies with the fraction of contaminant mass removed.

The framework quantifies decision confidence under severe information gaps.

Abstract

Decision making under severe lack of information is a ubiquitous situation in nearly every applied field of engineering, policy, and science. A severe lack of information precludes our ability to determine a frequency of occurrence of events or conditions that impact the decision; therefore, decision uncertainties due to a severe lack of information cannot be characterized probabilistically. To circumvent this problem, information gap (info-gap) theory has been developed to explicitly recognize and quantify the implications of information gaps in decision making. This paper presents a decision analysis based on info-gap theory developed for a contaminant remediation scenario. The analysis provides decision support in determining the fraction of contaminant mass to remove from the environment in the presence of a lack of information related to the contaminant mass flux into an aquifer. An info-gap uncertainty model is developed to characterize uncertainty due to a lack of information concerning the contaminant flux. The info-gap uncertainty model groups nested, convex sets of functions defining contaminant flux over time based on their level of deviation from a nominal contaminant flux. The nominal contaminant flux defines a reasonable contaminant flux over time based on existing information. A robustness function is derived to quantify the maximum level of deviation from nominal that still ensures compliance for each decision. An opportuneness function is derived to characterize the possibility of meeting a desired contaminant concentration level. The decision analysis evaluates how the robustness and opportuneness change as a function of time since remediation and as a function of the fraction of contaminant mass removed.