What If Analysis

As a first step in starting a tactical analysis program, the Office of Hazardous Materials can use the Bayesian network developed in this research to obtain an understanding of the relationships between the variables in a hazardous materials unloading release, based on their top categories. Based on the face validation study, the OHM does not have a complete understanding of the

relationships between the dependent and independent variables in an unloading release. A basic understanding of the relationships can be obtained by performing various “what-if” analyses in both a predictive and diagnostic manner. Predictive analysis involves setting the value of one or more known, or observed, explanatory variables as evidence in the network. The effects on the conditional probability distributions of the remaining, or unknown, downstream variables are determined by updating, or evaluating, the network. For example, the OHM may wish to investigate the impact of time of day on hazmat releases during unloading. Specifically, if the time of day for unloading flammable liquids is changed from the midnight to the daytime shift, a predictive “what-if” analysis can identify the impact on the quantity released and the monetary damages.

Using diagnostic “what if” analysis, a Bayesian network can be used to determine the distributions of the explanatory variables to identify the categories that are most or least likely given the outcome. For example, as part of a risk reduction study, the OHM may wish to analyze the ideal situation of zero quantity released and compare it to a medium-quantity release to understand the factors involved. Specifically, the OHM may wish to evaluate a zero-release versus medium-release situation in which the bottom of the basic packaging material failed, in order to identify possible risk reduction alternatives. To do this, the known variables, namely zero release quantity and the bottom of basic packaging material, are set as evidence, and the values of the remaining variables are updated as the explanations. This can be compared to a medium-quantity release to understand the differences. To make this comparison, evidence is set of a medium-quantity release to determine any changes in the distribution of the explanatory factors.

Exemption/Special Permit Analysis The use of the Bayesian network model to analyze exemptions, or special permits, at the OHM is a potential application of predictive “what-if” analysis. For example, suppose an exemption is being considered for the relocation of a lab with infectious substances from Rockville, MD to Fairfax, VA, which are suburban, commercial locations in the greater Washington, DC area. An exemption is likely necessary in this case due to the nature of the material being transported. Within an exemption, certain variables may be fixed, such as material type or location. In this scenario, the material type and locations are fixed. However, there are certain variables that may be set or prescribed by the OHM as part of the exemption granting process in order to minimize the possibility of an undesirable outcome. For example, in the previous scenario, the time of day or the season of the year for the relocation could be prescribed by the OHM in the exemption that is granted. Also, the type of container or packaging may be a point of negotiation or additional control for the OHM.(228) Given these non-fixed variables, the OHM can use the Bayesian network and “what if” analysis to prescribe such variables to minimize the probability of the consequences. For example, if the lab were relocated during the midnight shift in the summer months versus the daytime shift in the winter months, how would the consequences be impacted? Also, what is the impact on the consequences of various containers for transporting infectious substances? These questions can be answered by setting the fixed as well as the prescribed variables as evidence and updating the probabilities of the unknown variables, including the consequences.

However, in its present form, the Bayesian network model is specific to unloading incidents and the top material and container types. These include corrosives and flammable liquids, and fiber boxes and bottles, respectively. In addition, people-related outcomes are not considered by the present model, although this is the biggest concern when granting exemptions. Thus,

considering the lab relocation scenario above, the present model could not be made specific to infectious substances or the types of containers often used to transport infectious substances, such as plastic bags. Exemptions can get very specific in terms of the factors involved. Given this constraint, the existing Bayesian network model could still be used during the exemption approval process to gain very general insight into the occurrence of hazmat incidents. For example, how do daytime, suburban incidents impact the monetary damages in unloading accidents and possibly other scenarios? Additionally, assume the OHM has a concern about potential inadequate handling of containers. Using the existing model, the impact of dropping an improperly-loaded container on monetary loss can be determined and applied in a general sense to other situations. However, taking a different approach, the present model could be expanded, or new models developed, in order to provide a decision tool that specifically considers certain variables or categories, such as the infectious material type, infectious-material containers, or human-related consequences.

Analysis of Occurrence Spikes In addition to performing risk reduction analysis, diagnostic “what-if” analysis of a Bayesian network can be used to investigate occurrence spikes reported to the OHM. Increases in the occurrence of particular hazmat releases are often reported to the OHM from the field. An example of an occurrence spike might be many loose closures on bottles during flight. The OHM currently investigates such reports by using the HMIRS to determine similar reported incidents and the associated shippers for direct communication with them.(229) To illustrate the use of “what-if” analysis for occurrence spikes, assume there is a sharp increase in the occurrence of medium-quantity releases in which the bottom of the basic package material failed. The OHM can begin its investigation by getting a basic understanding of the distributions of the unknown factors associated with the occurrence spike using “what-if”

analysis. Specifically, the known variables, namely medium release quantity and bottom of the packaging material, are set as evidence to obtain the distributions of the unknown variables. The degree of prevalence of various categories of the unknown variables provides insight into the accident situation. These results may be compared to those for a zero-quantity release for possible additional insight. If additional information or explanation becomes known about the occurrence spike, it can be set as evidence to determine the impact on the remaining variables. It’s possible that additional information may make certain other explanatory variables more or less likely if the common outcome variable is known. Thus, known explanatory variables may “explain away” certain unknown explanatory variables, thereby making them more or less likely, given knowledge on a common outcome. In the example of loose closures during flight, the OHM may find it useful to know most likely categories for variables such as time of day, season of the year, or material type, which can be obtained using diagnostic style “what-if” analysis. The OHM may wish to obtain an overall characterization of an occurrence spike by running a

MAP. Using a MAP, the OHM can determine the most likely combination of variables

surrounding the occurrence spike. For example, which combination of unknown variables is most likely given failure of the bottom of the basic packaging material and a medium release of material? A MAP serves to tell a story of the events surrounding an accident. It characterizes an accident and answers the question, “What does the accident look like?” As a demonstrated example of a MAP, a MAP of the container failure variables in stages two through four, which were most influential to medium dollar loss, was run. A MAP can be run using a subset of the explanatory variables, thereby increasing the combination likelihood and potential usefulness relative to using many explanatory variables.(230) In the case of a release in which medium dollar loss occurred, it’s most likely that the container and its contents were improperly loaded and

dropped, thereby impacting the ground and encountering water or other liquid. In addition, the container was consequently punctured and crushed, leading to failure of the basic package material on the bottom of the container and the release of a small amount of material to the environment. This most likely scenario seems plausible. In the same way, MAPs can be used to gain insight into occurrence spikes. The MAPs for the variables in stages two through four given medium dollar loss across the five networks are summarized in Table 73. Each MAP had an occurrence probability of approximately 16.5%, and the categories of the MAPs were the same across the five networks.

Table 73: MAP of Most Influential Variables on Medium Dollar Loss.

MAP by Network

Explanatory

Variable T1 T2 T3 T4 T5

CO

Floor and

water/liquid water/liquid Floor and water/liquid Floor and water/liquid Floor and water/liquid Floor and CA Improper loading and dropped Improper loading and dropped Improper loading and dropped Improper loading and dropped Improper loading and dropped FIA Basic package material on bottom Basic package material on bottom Basic package material on bottom Basic package material on bottom Basic package material on bottom FM Punctured and crushed Punctured and crushed Punctured and crushed Punctured and crushed Punctured and crushed

RQ Small Small Small Small Small

MAPs for medium release quantity were also run to characterize the worst case in terms of

quantity released. The results are similar to those for dollar loss and are given in Table 74. Based on 4/5 of the networks, a medium-quantity release is most likely characterized by an improperly-loaded and dropped container and contents that impact the ground and encounter water. However, while the container is most likely punctured and crushed, it’s the basic package

material on top of the container that fails in the case of medium release quantity. The Office of Hazardous Materials may find it useful to compare MAPs for insight into various events or problems, as was done here for dollar loss versus release quantity.

Table 74: MAP of Most Influential Variables on Medium Release Quantity.

MAP by Network

Explanatory

Variable T1 T2 T3 T4 T5

CO

Floor and

water/liquid water/liquid Floor and water/liquid Floor and water/liquid Floor and water/liquid Floor and CA Improper loading and dropped Improper loading and dropped Improper loading

and dropped other

Improper loading and dropped FIA Basic package material on top Basic package material on top Basic package material on top Basic package material on bottom Basic package material on top FM Punctured and crushed Punctured and crushed Punctured and crushed other Punctured and crushed

Another decision support query useful for occurrence spikes is the determination of the most influential variables given the occurrence of another variable. For example, suppose a risk assessment engineer is focusing on incidents involving the “floor and water/liquid” combination, perhaps because there has been a recent increase in this particular causing object. Given that an incident has the “floor and water” combination as its causing object, what variable should the engineer next pursue to best impact medium dollar loss? This can be determined in GeNIe by setting the “floor and water” combination as evidence in the Diagnostics module and then updating the rankings of the remaining variables. In this example, the engineer should focus on the contributing action that led to the failure, based on its being the next most influential variable in 5/5 networks. This query involved a conditional analysis between two variables. Using

variables for the occurrence of a particular category of dollar loss. This can be done in GeNIe by converting the Bayesian network to an influence diagram and assigning utilities to the categories of dollar loss.(231)