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Joint Infrastructure Interdependencies Research Program Symposium

Public Safety Canada

National Sciences and Engineering Research Council

MARCH 6, 2007 CANADIAN EMERGENCY MANAGEMENT COLLEGE

OTTAWA, ONTARIO

Symposium Report

MANSELL, RANKIN AND ASSOCIATES 198 HOLMWOOD AVENUE

OTTAWA, ON, K1S 2P4

TEL: 613.232.6423 IIV

551.5 .C2 S965 2007

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Joint Infrastructure Interdependencies

Research Program Symposium

Public Safety Canada

National Sciences and Engineering Research Council

MARCH 6, 2007 CANADIAN EMERGENCY MANAGEMENT COLLEGE

OTTAWA, ONTARIO

Symposium Report

LIBRARY I EIBLIOTI.IF: PSEPC/SPPCC

OCT 3 1 2 012

OTTAWA (ONTARIO)

K1A OP8

MANSELL, RANKIN AND ASSOCIATES 198 HOLMWOOD AVENUE

OTTAWA, ON, KIS 2P4

TEL: 613.232.6423

Joint Infrastructure Interdependencies Research Program March 6, 2007

Table of Contents

1. Introduction 2

2. Opening Remarks 3

3. Presentations 4

3.1 Simulation of Infrastructure Interdependencies Dynamics for Disaster Response Coordination.

3.2 Modelling Infrastructure Interdependency for Emergency Management Using Geographic Decision Support System 8

3.3 Interdependencies and Domino Effects in Life-Supporting Network 12

3.4 Developing a Model of Infrastructure Interdependencies 16

3.5 Models that Simulate Critical Infrastructure Networks 20

3.6 Improving the Resilience of Water Infrastructure and Health Response Systems Against Waterborne Diseases 23

4. General Discussion Period 27

Manse!!, Rankin and Associates


1. Introduction

On March 6, 2007 the Department of Public Safety Canada (PS Canada) hosted the second annual symposium of the Joint Infrastructure Interdependencies Research Program (JIIRP). The objective of the Symposium was to present high-level summaries of the research findings and progress of the six research projects that comprise JIIRP.

JIIRP is part of a national effort to ensure Canada is secure from threats and vulnerabilities that have increased due to the evolving complexity and interconnectedness of its critical infrastructure. The Program is intended to produce new science-based knowledge and practices to better assess, manage and mitigate risks to Canadians from failures related to critical infrastructure interdependencies. JIIRP is jointly funded by PS Canada and the National Sciences and Engineering Research Council (NSERC). The symposium brought together researchers, academics, government officials and other stakeholders to give them the opportunity to become acquainted with the research underway and to meet and network with others who share similar research interests.

This document summarizes the key points that were made by the researchers during their presentations to the symposium participants. It also captures the introductory remarks made by the symposium's co-hosts, Kevin Phillips, Executive Director of the Canadian Emergency Management College, PS Canada and Barbara Muir, Director, NSERC. In addition, the report details the comments and discussion points that were raised during the question and answer periods.

(Note: This report has been written for the benefit of a non-technical audience. For further information on the content of the researchers' presentations and for the mathematical models, please refer to http://publicsafety.gc.ca to view the PowerPoint slides each researcher assembled for the symposium)

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Joint Infrastructure Interdependencies Research Progranr March 6, 2007

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2. Opening Remarks

In their opening remarks, Kevin Phillips and Barbara Muir underscored the value of theresearch underway and the importance of collaboration within the research communityand with stakeholders that are connected to major infrastructures. Listed below are thekey messages that were shared during this opening session of the symposium.

Kevin Phillips, Executive Director, Canadian Emergency Management College,Public Safety Canada

• JIIRP is developing new knowledge by expanding research in the area ofinfrastructure interdependencies.

• The research undertaken through this Program continues to be an importantcomponent of our national capacity to protect Canada's critical infrastructure.

• JIIRP has succeeded in raising awareness of the need to support the investigation ofcomplex interaction points and practical research focusing on interdependencies.

• The research projects underway continue to build important linkages, partnershipsand networks across Canada with key stakeholders that are connected to majorinfrastructures.

• This Symposium provides participants with an important opportunity to learn aboutthe research underway and the progress that is being made across the country.

Barbara Muir, Director, National Sciences and Engineering Research Council

• When PS Canada first approached NSERC there was an immediate interest incollaboration as there was no funding allocated at the time in the area ofinterdependency of critical infrastructure. Fresh in everyone's mind was thewidespread power outage and its impact on critical systems.

• NSERC is now investing approximately $6 million per year in the area of safety andsecurity. It is hoped what has been started with JIIRP will be continued and capacityin this area will grow.

• Research collaboration and the interdisciplinary nature of these projects is a long-termundertaking and an important aspect of what JIIRP is trying to achieve.

• The Symposium creates an opportunity to share information, create synergies andmoves JIIRP beyond six isolated projects to a more collaborative program.


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3. Presentations

3.1 SIMULATION OF INFRASTRUCTURE INTERDEPENDENCIES DYNAMICS FOR

DISASTER RESPONSE COORDINATION

University of British Columbia Team led by Jose Marti (Prolect Leader)

Overview of the Project• Towns, municipalities and organizations are often not aware of the (hidden)

interdependencies of their critical infrastructure and there is no mechanism todetermine how their critical infrastructure system will react during an emergency, orwhat vulnerabilities exist in the system. In addition, there is no way to validate andoptimize emergency response plans and decision making related to criticalinfrastructure.

• As a result, unexpected interdependencies appear during an emergency or disasterthat can cause emergency response plans to fail - sometimes catastrophically. Sub-optimal decisions are made which can have a direct impact on the number of livessaved.

• The mandate of the project is to develop innovative solutions to mitigate largedisaster situations involving multiple infrastructure systems.

• The Critical Infrastructure Interdependencies Simulator (12Sim) has been created toallow users to model the critical infrastructure and the associated interdependencies ofa town, municipality or organization. An emergency event can be simulated and a fullsimulation executed to see the effects on critical infrastructure.

• The simulator can model the effect of decisions made by emergency managementcommand and control and different scenarios can be executed in order to select thebest course of action for emergency response planning or execution. Simulations canbe connected together to model interdependencies between larger entities at aprovincial and national level.

• Through the use of this tool, municipalities and organizations can validate and testtheir critical infrastructure and optimize emergency response plans.

• To date, 80 hours of partners have contributed assistance and two workshops withindustry have been organized. The project has generated 37 internal documents, nineconference papers and two journal publications. The project team consists of 13researchers, six doctoral students, seven masters students and two post-doctoralresearch associates. A project website has been created for knowledge dissemination(http://www.ece.ubc.ca/-jiirp/) and a wiki is online for collaborative research.

I2Sim - How Does It Work?• Modern infrastructure systems are very complex with multiple critical nodes and

hierarchies. The I2Sim architecture begins with a human readable table and thentranslates the data into systems theory. In order to show the interconnectedoperations, the system is divided into three basic components:


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—A 'cell' is the production component. A hospital is an example of a 'cell' that requires inputs (such as electricity, water, doctors, medicines) and generates outputs (such as healed patients).

—A 'channel' is the transportation component. Wires carry the electricity to the hospital, pipes carry the water, and the doctors are carried by the transportation system.

— 'Dispatching' is the decision component. It is the decision made that determines resource allocation during a time of scarcity.

• Events that change the system are then taken into consideration: disasters (i.e. an earthquake), psychological responses (i.e. panic), human processes (i.e. bureaucratic red tape) and human decisions (i.e. distributor ratios during times of limited resources).

• Two indices — 'operability' and 'resource availability' combine to indicate 'wellness'. — Operability ("m" factor): Assuming full inputs are available, a power substation

is designed to deliver an output of 200 MW but because of earthquake damage can only deliver 100 MW has m = 0.5

—Resource Availability ("r" factor): Reduced cell output which is not due to reduced cell's performance but to scarcity of resources. For example r = 0.7 means that due to lacking input or reserve resources the cell will only be able to produce 70% of its capacity even though m=1

— Wellness is derived from operability and resource availability and can deteriorate by a reduction in either or in both indices.

• The I2Sim merges together planned scenarios, simulation and reality in a very fast, real-time platform. Interactive scenario playing is basically simultaneous with I2Sim generating solution speeds between 1,000 and 10,000 times faster than standard (non-linear) simulators.

Psychological Issues — The Human Layer • Additional work is underway to use I2Sim to link psychological issues with the

physical layer. • The effectiveness of the simulator relies on its ability to identify infrastructure

weaknesses and interdependencies, relief weaknesses and interdependencies and resource allocation. Because these issues predict collective efficacy across social geography they support the human capacity to cope and act during a disaster.

• The initial focus for psychological issues is on population-related factors, such as large-scale psychological issues and those 'victimized' by the disaster. Population factors can vary as a result of vulnerability characteristics (such as access to disaster support and services; beliefs regarding consequences and probability) and psychological characteristics.

• First responders, such as health care professionals, police, fire fighters, community workers and volunteers, will be taken into consideration at a later stage in the project.

UBC Campus Case Study • The University of British Columbia campus is being used as a case study for the

I2Sim due to the fact that the campus shares many attributes of a small city — it has 47,000 daily transitory occupants, 10,000 full time residents and its own utilities

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providers and infrastructure. Information is readily available and it is an optimalstarting point before moving forward to model larger regions.

• The intent in working with UBC is to assist them in evolving from a culture ofreaction to a culture of preparedness.

• The I2Sim analysis follows a well defined logic, as summarized below:- The case study began with the identification of data sources followed by data

gathering through structured and unstructured interviews, participantobservations, analysis of functional schematics, and analysis of operationalprocedures.

- The information is then inputted to allow for GIS modeling and to develop acampus risk matrix. Channel and cell modeling follows along with thedevelopment of scenarios, including the analysis of emergency plans, estimatedresources and estimated impact.

- From there, a damage assessment is compiled that takes into considerationstructural and non-structural analyses, lifelines and a performance analysis.

- The final phase is the activation of the simulation which includes a simulation offailures, quantification of functionality, identification of islands, channels andcells status and a`what if' analysis.

• As an example, the policy interdependencies simulation across the UBC campus wasusefiil to identify weakly coupled policies across agencies as well as hiddenknowledge and unused resources. It can also be used to help in the preservation ofinstitutional memory.

• I2Sim can also provide visual aids for infrastructure interdependencies inemergencies. GIS tools are excellent for visualizing snapshots of evolving events andnew collaborative visualization tools allow for `what if scenario playing, time/state,(un)certainty of data and novel display environments.

Feedback From Industry• The industry partners working on this project have invested a great deal of time and

effort into the creation and evolution of the simulation. Approximately 80 hours ofmutual assistance with partners has been logged in areas such as assistance inbuilding models and contributing to a wiki for collaborative space.

• A number of issues and challenges have been identified by industry and are currentlybeing taken into consideration as the project evolves:

- Greater definition is required regarding coherent infrastructures risk tolerancelevels from natural disasters;

- Processes and policies regarding the protection of critical infrastructures need tobe standardized;

- Standardization in the definition of critical infrastructures across federal,provincial and local governments is required;

- The prioritization of critical infrastructures across all levels of government needsto be standardized;

- Common and agreed protocols for the sharing of critical infrastructuresinformation during an emergency should be developed.



• On a positive note, the benefits of sharing information amongst various levels of government and industry are significant. The database of simulation scenarios can also preserve institutional memory after experts or key people retire.

Continued Developments • The project is moving forward to integrate visualization and decision support

systems. • Extensions of the simulator and support systems are moving forward with the City of

Vancouver, the City of Richmond, Vancouver International Airport, Vancouver 2010 and a multi-cities coordination across the GVRD.

• Data exchange standardization is also in development as is multi-layered coordination.

Question and Answer Period

Question: To what level can this model be expanded? How complex can the model grow? Capability has been a significant focus of the project. The simulator can currently deal with systems at 40,000 variables and nodes at real time speeds. It is an instantaneous, linear model.

• We can cope with very large systems in a responsive manner.

Question: Is this model going to be shared with industry and emergency medical organizations? • Yes. It's a tool that can and should be used by both. • Work is underway to commercialize the product and communicate its potential value

to clients. A dedicated lab would allow for additional simulations and the creation of a common, shared database would benefit all clients.



3.2 MODELLING INFRASTRUCTURE INTERDEPENDENCY FOR EMERGENCY MANAGEMENT USING GEOGRAPHIC DECISION SUPPORT SYSTEM

Oiuming Cheng, York University

Overview of the Project • The main objective of the project is to develop a geographic decision support system

for representing, analyzing and modeling infrastructure interdependencies for emergency management.

• The research has been planned according to three main phases: - Phase I (first year): Theoretical and conceptual modeling; - Phase II (second year): Models and methods development for representing,

analyzing and modeling infrastructure interdependencies; - Phase III (third year): System integration and validation.

• The project is currently in Phase II and a scenario approach has been adopted based on two types of disasters: floods and fire.

• Today's presentation focuses on water-related disasters beginning with models to predict rainfall run-off and flooding events. A localized infrastructure interdependency analysis and a flood disaster risk mapping and visualization is then undertaken in order to develop a SDSS modeling and optimum response analysis. The output of the analysis provides a minimum risk prediction, an uncertainty assessment and localized visualization.

River Flow Prediction Model • The Greater Toronto Area has been used as a case study to predict flows in river

basins. • Different methods have been developed, both linear and non-linear, based on runoff,

precipitation, watershed related parameters (such as soil type, basin shape and basin slope), climate related parameters and time.

• The outcome of these models is the prediction of storm run-off in gauged and ungauged basins. The outcomes can be presented both empirically and visually through online mapping capabilities.

Flooding Prediction Model • A flood as a nature event can be described as the rising of a body of water and its

overflowing onto normally dry land; a (usually disastrous) overflow of water from a lake or other body of water due to excessive rainfall or other input of water.

• Two models have been developed to predict flooding. A traditional linear model has been tested which provides rise and recessions properties which will then allow for the calculation of the depth, speed and velocity of the flood.

• A new non-linear model for the prediction of river peak flow and recession processes has also been developed that allows for better estimates of the parameters of the risk assessment (flooding depth, flooding duration, flood wave velocity and rate of rise/recession of water level).



Flooding Disaster Risk Mapping and Visualization — Fuzzy Them for Risk Assessment • Significant work has been undertaken to explore the utility of fuzzy set theory to

flood analysis. Three spatial fuzzy performance indices have been developed: —combined reliability-vulnerability index; —robustness index; —resiliency index.

• Fuzzy performance indices have been applied to a spatial flood analysis of the Medway Creek in North London, Ontario. The case study has also integrated GIS with the fuzzy reliability analysis.

• The case study concluded that flood management is exposed to a diversity of uncertainty sources. A probabilistic approach to the analysis of floods fails in cases of human error, subjectivity and lack of history. A fuzzy flood reliability analysis offers an alternative approach.

Localized Infrastructure Interdependency Analysis — Petri Net Tool and Fragility Curves Analysis • An infrastructures system is the underlying foundation or basic framework of a

community, including all the basic facilities, services and installations needed for the proper functioning of the community.

• In a system, multiple infrastructures are connected at multiple points through a wide variety of mechanisms. Change in one infrastructure may lead to change in another infrastructure. This mutual dependency on each other is termed interdependency.

• In Canada, many roads, bridges, water distribution and sewer networks, public buildings, dams and dykes are now 50 years old and some pipe networks are more than 100 years old. During the 1960s, 4.5% to 5% of GDP was being spent on infrastructure projects; spending has now declined to 2%.

• In order to analyze infrastructure interdependencies, a 'system of systems' perspective is needed, recognizing that systems such as transportation, electricity, natural gas, oil, water and telecom all have various levels of interdependency.

• The different types of infrastructure interdependencies and associated failures are what create complexity within the system.

• Four types of infrastructure interdependencies have been identified: —Physical interdependencies: Occur between two mutually dependent

infrastructures. —Cyber interdependencies: State of one infrastructure depends on the information

transmitted into it through the other information infrastructure. —Geographic interdependencies: When a local environmental change affects the

other infrastructures close to it. —Logical interdependencies: Infrastructures are linked through financial markets.

Change in one may cause change in others without any direct physical, cyber or geographic connection.

• In addition, three types of failures of infrastructures have been identified: — Cascading failure: Disruption in one infrastructure causes disruption in a second

infrastructure, which subsequently causes disruption in the third infrastructure.

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- Escalating failure: The existing disruption in one infrastructure exacerbates an independent disruption of a second infrastructure, generally in the form of increasing the severity or the time for recovery or restoration of the second failure.

- Common cause failure: Two or more infrastructure networks are disrupted at the same time; components within each network fail because of a common cause.

• Petri Net theories have become one of the dynamic tools for modeling systems in a network and their interdependencies. The Petri Net Model looks at transitions between 'places' with corresponding input and output functions. Transitions can act on input tokens by a process known as firing.

• A transition is enabled if it can fire. When a transition fires, it consumes the tokens from its input places, performs a processing task and places a specified number of tokens into each of its output places.

• Not only have direct associations been modeled, but the hidden, cascading effect has also been taken into consideration.

• Highways, dams and other infrastructures can be represented using Petri Net modeling allowing for complex connections between each node.

SDSS Analysis Techniques for the Modeling of Infrastructure Interdependencies • Work is underway to develop a fuzzy relation analysis method implemented in GIS

for modeling infrastructure interdependency for emergency and disaster management. • The model acts as a decision support tool to identify the statistical connections

between nodes and to calculate the interdependencies among them. • Direct versus indirect dependencies can be identified and estimates of their relative

importance can be assessed. • From there, decision makers are able to rank the relative importance of infrastructures

during disaster situations. A recognition of which infrastructures are the least and most important will be provided as well as which have the greatest impact on other infrastructures.

Conclusions • The project has been designed to answer the following questions:

- What is the relative importance of infrastructures involved? - What set of nodes are essential in controlling the cascade influence on the whole

system? - Given a set of infrastructure networks and a critical function, what is the subset

of critical nodes across all networks that will have an adverse impact on the interdependencies of a set of infrastructure networks due to direct or indirect dependencies?


Question: Are you discovering different outcomes than expected? Which nodes are the most critical and should receive the most investment? • A decision support model identifies which sectors should have priority at a given time

and place.


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• It's a dynamic model based on time, place and events. For example, the priority areasat the commencement of a flood may be different then those at the end of the flood.

• This model can suggest a ranking of priorities and identify within that network whichnodes/locations are the most important.

Question: Do you have any results yet?• No. The project is currently in the artificial data stage to validate the model. The next

phase will use a real data set.• The project has great potential for policy makers but it is still one phase away from

the ability to draw general conclusions.

Question: Have you ever run all of the models together or are they used separately?• Each model is run separately. The next stage will involve running them together.

Question: Will it be one-way or two-way coupling?• It will involve two-way coupling.

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3.3 INTERDEPENDENCIES AND DOMINO EFFECTS IN LIFE-SUPPORTING NETWORK

Benoit Robert, École Polytechnique de Montréal

Overview of the Project • The focus of the study is on the vulnerability of life line infrastructures. All

infrastructure networks are interdependent and, to a certain extent, rely on each other to operate at optimal efficiency. This interdependence adds an additional layer of complexity to the ability to make decisions in emergency situations.

• This study is focused on a strategic zone of Montreal — the downtown core — where significant economic activity is concentrated and in which key partners operate, including Bell Canada, Hydro Québec, Transports Québec, GazMétro and the City of Montréal. Each stakeholder must work within this zone to evaluate operations and set priorities during an emergency situation.

• The core principle behind the project is to ensure that stakeholders work in cooperation to maintain a principal of good faith and communication.

• A significant amount of interdependency information has been collected which has allowed for a detailed description of all entity relationships.

• Working with large amounts of data has created major challenges in information management. A break through has been made with fuzzy mapping that has resulted in the creation of new tools.

Identeing the Interdependencies with Netevorks • Organizations or entities can be represented as having functions, operations and

internal resources. These entities also use external resources and furnish resources for other entities to use.

• A major parameter in the calculation of interdependencies and vulnerabilities was time. Due to the interdependency of networks, problems can escalate over time and a domino effect will occur. There is a need to be able to anticipate these problems and apply mitigation measures to avoid further deterioration to the system.

• When studying failure and reconstruction within networks, backup resources were also taken into consideration as most companies have access to secondary generators that are put in use during emergency situations.

• All of this interdependency information has been collected which allows for a detailed description of the interdependencies among networks.

• The network interdependencies have been illustrated using a matrix. The matrix can show where two entities are operationally dependent upon each other and what type of interdependency exists.

Challenges Associated with Information Management • Major challenges have developed in the collection and analysis of information. At

times, the huge volume of information became difficult to manage.


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• More importantly, the nature of information required for this project comes withsignificant confidentiality parameters. Many partners have objections to sharinginformation that is confidential and critical to the functioning of their operations.There is fierce competition among networks and care had to be made not tojeopardize the competitive position of organizations. For example, no private sectorpartners wanted maps to become public knowledge.

• Difficulties also arose in trying to use information that had been badly interpreted bymanagers at the source level.

• Additional challenges arose due to a backlog of information.• In order to manage these challenges and move forward with the project, agreement

was reached in a number of important areas:- The researchers will work at a general, as opposed to a specific level;- No sensitive technical information will be published in common;- Specific, pertinent information to interdependency problems has been requested.

This information is used for analysis and not kept in and of itself.• A significant effort is also required to continue to update the information. The private

sector is constantly evolving and analyses that are based on old information canquickly become ineffectual.

• The deterioration of resources over time also needs to be taken into consideration.• A new challenge has become apparent over the past year specific to the City of

Montreal. Who is going to manage the interdependencies? It would generally be apublic safety bureau, but at the municipal level this presents a problem.

The Fuzzy Mapping Strategy• The fuzzy mapping strategy has provided a flexible model to characterize and

develop tools to manage interdependencies.• The project has used specific information but is taking a global approach to the

challenge of infrastructure interdependencies.• The exact number of infrastructures has been mapped to date and work has

commenced in the classification of vulnerabilities of the infrastructures.• For example, within the area of drinking water, the number of infrastructures has been

identified which then allows for an analysis of various interdependencies andvulnerabilities within the drinking water network. Work is also underway to identifywater alternatives (such as bottled water) and the challenges associated on the supplyside of infrastructure interdependencies. A forecasting approach has been applied tothe problem to identify how long it will take during an emergency situation before theregion runs out of water and then a model was used to identify what the crisis point isin the City of Montreal.

• This exercise has also been done in the telecommunications and electricity sectors.

Validation of Methodology• The project team will be working with the civil security bureau of the City of Quebec

to provide them with technical support to manage decisions.• The scope of the project will encompass the entire municipality and will involve all

services, including roads, sewages, telecommunications, gas and drinking water.


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• This project provides an opportunity to test the methodology that was developed when working with the City of Montreal. The goal is to build a methodological guide that can be followed by any municipality.


Question: With respect to security and privacy, can you dare how detailed the information from companies needs to be? • The project team uses a global approach that is refined at the company level. We

develop the matrix that captures the requisite information but the data is produced internally by the company and then validated by the project team.

• The approach is to ask a company for the specific information that is required and have them generate it internally in order to be able to move forward with the analysis.

Question: You've noted the various timelines that rise up in crisis management. There are some peak times that become particularly important during an emergency. Did you model peak traffic periods for public transit? • We have not been able to look at that specific problem. • One of the surprises that emerged from the research is that we tend to think that

others will be immediately affected when problems arise in one network. In reality, most networks have back up resources that allow for the problem to be managed for a certain period of time.

• Our inethodology helps to plan for problems, forecast what can be expected to arise and then prepare emergency measures accordingly.

Question: Is the model flexible enough to incorporate industrial risks, such as an environmental disaster in a certain sector? • Right now we're working strictly with interdependency and testing models with

municipal civil security. • These scenarios can involve various risks that are being incorporated into the

methodology and project management but it has not yet been applied it to other problems and not to industrial models.

• However, a key benefit is that people can keep their own information and use the methodology that we have developed and tested. One of the basic principles of the project is a climate of collaboration.

Question: Were the effects of a pandemic on personnel included in your model? • Human resources are one of the factors that are taken into consideration within the

functioning of an organization. • We are currently applying the model to Quebec City to better understand the

problems that arise due to human resources. For example, we hope to be able to see the impact of transportation on human resources. This component of the project will commence in six months.

Question: How will your work help an emergency manager who wants to mitigate the catastrophic impact that an emergency can have on an organization?

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• The objective of this project is to try to determine whether some infrastructures are more critical than others.

• For example, there is a major telecommunications infrastructure and it is critical for the entire Bell network. It also has an impact on other sectors, that in turn have an impact on other networks. Networks are planned so that if one falls the others may continue.

• This project provides an opportunity to draw up new protocols for interaction amongst networks. There is no need for a project team at a university to micromanage industry. However, we can provide the methodology and information hub to connect various networks. They can then work directly together on sensitive points.

Question: Do you use a specific risk method or do you just take what companies tell you about their priorities? • The challenge is to define the situation properly. Our current methodology focuses on

what is most important to the public within a public safety/emergency situation. • The methodology takes into consideration many elements that may not be important

to our partners in isolation but that are important to the public and necessary to protect citizens.



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3.4 DEVELOPING A MODEL OF INFRASTRUCTURE INTERDEPENDENCIES

Jinyue Zhana (on behalf of Prof. Tamer El-,Diraby), University of Toronto

Overview of the Project• The objective of the project is to develop an ontology to cope with interdependency

issues for Canadian infi•astructure.

• Why is an ontology needed? Industry faces various complexities when dealing withinterdependencies. The infrastructure domain involves several heterogeneousengineering, business, and social systems controlled by a hierarchy of governmentdepartments and private enterprises.

• In some cases, these systems are interlinked and interdependent; in other areas, theyhave their own data models that cannot communicate with each other.

• The needs of industiy are dynamic, the stakeholders from various domains havedifferent needs and interests and due to the long lifetime of infrastructure, the needsof one organization may vary at different stages.

• An additional component of the project deals with the issue of scalability. Thevolume of information or knowledge is aggressively growing, as well as the needs ofinformation. Work is underway to identify methods to manage this complexity.

• To properly address these complexities, especially the interdependency issues, aninteroperable information system is needed which allows a seamless exchange ofinformation and knowledge regarding products, processes, constraints andmechanisms among various stakeholders.

• The key to interoperability is semantics - the meaning of the words people use withinindustry. Ontology is a method to guarantee a shared conceptualization of a domain;it is a common language shared among industries working within a domain.

Tasks• The ontology project is divided into tasks. Task I is to develop an integrated business,

engineering and legal profile for stakeholders, including their information needs,risks, constraints and competencies. A of map the constraints on all stakeholders isalso needed. (Constraints are the regulations that define work system boundaries.)

• Task 2 is to model infrastructure management scenarios - how to manage the design,operation, and crisis of infrastructure systems.

• Task 3 is the corner stone of this research project - the development of an ontology.In order to develop the ontology, the interdependency transactions amongstakeholders must be studied. Then an understanding of `who asks for what' and `whois responsible for providing that' is required. This will reflect information flow andresult in the drafting of a liability map. Then the ontology will be developed fromthree perspectives: product ontology, process ontology, and actor-role ontology.

Current Status

• Task 1: Boundary conditions and stakeholders' profiles.- One master student has started working on this as of July 2006.


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• Task 2: Modeling infrastructure management scenarios.- It is divided into sub tasks, and one sub task that has been done is the

identification and categorization of major interdependency linkages betweenvarious industry sectors.

• Task 3: Interdependency ontology.- The research for the interdependency transaction model has been started at

University of British Columbia by a PhD student and the team at the Universityof Toronto has undertaken a significant amount of work on the remainingcomponents.

• Task 4: Grid-enabled information exchange- The University of Regina is still looking for an appropriate candidate for this

component of the project.

Ontology Overview• Semantic systems offer effective means for addressing interdependency issues by

representing complex domains using ontologies.• An ontology is an explicit specification of a conceptualization. It is composed of three

elements: (1) a taxonomy of concepts which is used to create a common vocabulary,(2) a set of relationships to link concepts, and (3) a set of axioms to control conceptbehaviour.

• Ontology supports machine learning and processing of data and is therefore effectivein bridging the interoperability gap. It also supports a multi-dimensional viewing ofknowledge, so it is able to address the dynamic needs of projects and organizations.In addition, it provides the means for representational extension - it supports scalableinformation needs.

Product Ontology• Infrastructure product ontology needs to fiilfill the following requirements:

- Represent the notion of composition and aggregation among infrastructureproducts;

- Represent the notion of product similarities across infrastructure sectors;- Represent the similarities in function that products in various sectors exhibit;- Differentiate static versus dynamic characteristics of products;- Differentiate subjective versus objective characteristics of products; and- Represent the notion of semantic similarities between concepts within a sector

and across sectors.• The first step in building an ontology is to group the concepts. For infrastructure

products we categorized them into four groups: electricity, gas, water, andwastewater. Some other categorizing criteria are also addressed, for example,similarities across sectors and common product functionalities

• For some supporting concepts, like product attributes, mechanisms, and constraint,the ontology needs to address not only the clustering of those concepts, but also howto map between supporting concepts and physical products.


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Process Ontology • A process ontology is being built which will answer the following questions:

- What are the processes? - Who performs the process? - What constraints are associated with the process? - What is the function of the process? What does the process do? - When is the process performed? At what phase? - What is the life cycle of the process? - To what domain does the process belong? - To what sector does the process belong? - To what area of expertise does the process belong?

• The major application of process ontology is the business process management for infrastructure. With the help of Lattice theory and formal concept analysis, two or more ontologies can be merged for information exchange.

• For example, if information exchange is needed between two ontologies, FCA algorithms would be used to link the two ontologies by producing a merged lattice(s) that combines the two ontologies, allowing for a seamless exchange of information.

Actor-Role Ontology • The actor-role ontology focuses on the human factors inherent in dealing with

infrastructure interdependencies. An actor-role ontology addresses the following questions:

- Who is involved and what is their relationship? - Who plays what role(s)? - What are the responsibilities of a role? - What interests does a role have? - What are the requirements of being a role? - What kind of information does a role need? - When does the role need it? - I have a piece of information, to whom should I send it out?

• The major application of the actor-role ontology is to build a p2p information system with multi-agent technology for the exchange of infrastructure interdependency information.

• A software agent will classify system users according to role definition by domain knowledge (ontology) and find attributes associated with his/her roles.

• A collection agent will send a request to a search agent after integrating the attributes (responsibility, interest, etc.) inferred by his/her role and attributes specified in his/her profile.

• A search agent will conduct a semantic search on an indexing server to find relevant information over the P2P network and locate those resources.

• A download agent will take care of getting relevant information from peers for the user.

• This kind of information collection will be an on-going process even if the user is offline. Newly collected information will be prompted to the user whenever he/she logs on to the system. Users also can actively query the system if they have a special interest that is not specified in their profiles.


Joint Infrastructure Inter•dependencies Research Prograrrr March 6, 2007

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• Users can contribute to the system by uploading meta data of their information itemto the indexing server. A dedicated agent will perform uploading and indexing. Alearning agent will observe the use pattern of a system user and update his/her profileaccordingly.

Conclusions• The objective of the project is to develop an ontology for infrastructure

interdependencies.• This is being done through multiple tasks:

- Modeling infrastructure dependency from different perspectives;- Build an ontology;- Apply, monitor and refine;- Ontology synthesis and grid application.

• Additional information can be found at www.i2c.ca.


Question: What about the legacy software existing in the system?• This will provide a common language to describe functions for critical infrastructure

and will bridge the communications gap across legacy software.

Question: What progress has been made in the last year?• The ontology for product and processes has been finished and we are in the practical

application stage to facilitate routing problems.

Question: You were implying that your intelligence agents were going to be goingthrough to index the nzeta data that was being stored. How is meta data going to becreated and assigned?• A P2P system will be used.• There are still many issues regarding security and rights that need to be addressed.


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3.5 MODELS THAT SIMULATE CRITICAL INFRASTRUCTURE NETWORKS

Wenium Zhang, University of Saskatchewan

Overview of the Project • There are two key components to the project:

—The first is to develop a model that will capture all interdependent relations in a critical infrastructure (CI).

—The second is to create a simulation of the CI interdependencies. • To help develop the model, researchers have used historical data, developed a model

based on the data and then generalized the model. • The potential users of the tool that will come out of this project will be key decision

makers in government agencies.

Project Background • The focus is on the modeling and simulation of networked CI systems for

preparedness and response. • A science-based approach has been undertaken to study CI as a system that is

composed of basic elements, including structure, behaviour and function. • When modeling a networked CI system the first two questions which much be asked

are 'what is it?' and 'how does it work?'. Then, an analysis of how to manage the system can be undertaken, with management being divided into preparedness scenarios and emergency response. The outcome is the development of policies and procedures to govern the management of emergency situations.

• A simulation can help a policy maker better understand the consequences of a decision while in a preparedness stage, before making real-time decisions during an emergency response situation.

Progress To Date — Model For 'What' • A framework has been proposed based on six elements that are found in all systems —

function, behaviour, state, effect, principle and structure. In addition, each element within a system has these attributes.

• This structure can be applied to any CI system based on the principle of decomposition to support autonomous management, recognizing that in reality there are several owners of a system (various levels of government and private sector industry) each with their own decision making processes and management.

• The challenge is finding a way to integrate various models to allow management to make timely infrastructure decisions. In response, the project has integrated all the models into a generic model in order to identify vulnerable areas of a networked CI system.

• Social network theory, common within the field of sociology to study human-human interaction patterns, is being used to rank the importance of CI's. The principle of centrality, within the social network theory, produces a ranking of the most important CI's based on the following propositions:

—The higher centrality degree of a CI, the more interdependent;



—The higher the level of interdependence of a CI, the more critical, thus the more vulnerable;

—The more inputs into a CI the more others depend on it; —The more outputs produced by a CI the less it depends on others.

• As an example, Saskatchewan has produced a national ranking of the importance of CI systems in Canada with energy and utilities at the top of the list, followed by communication and services. An analysis using the social network theory results in a different outcome with electricity being the most important, followed by gas and then information and communications technologies.

Progress To Date — Model For 'How' • A Petri Net model is used to study how the CI system is performing and brings all of

the flow dynamics together. • However, the key to modeling how the CI system will perform is to start with a

generic model that will then generate the Petri Net model. A generic model provides an easy-to-understand human interface for the purposes of decision making and simulation.

• The decision maker can work with meaningful information in order to move forward with planning and management actions. This will ensure a decision maker does not need to understand the technicalities of the Petri Net model.

• The Petri Net model provides the quantitative analysis, however the generic model allows a decision maker to focus on the issues that are of the highest importance to him or her.

• A test bed is in development that allows the decision maker to interface with software that shows the information and data most useful to planning and management with a two-window concept to support Petri Net modelling.

Conclusions • In general, progress to date has shown great promise to create a tool to support policy

makers. The following recommendations have emerged: —It will be necessary to have a generic model for each networked CI system. —Model integration methodology should be followed to develop any preparedness

and response management systems —A ranking of critical infrastructure in terms of interdependency should be

carefully exercised — the proposed method based on the social network theory should be followed.

—Computer technology has shown promises in safety and security with particular reference to the two-window concept.

—The relationship among the owners of CI and provincial/federal gove rnments needs to be studied. Currently, there is a tension betvveen the two. From the perspective of owners, safety is the business of government and can compromise competitiveness. Governments believe that safety can enhance competitiveness and they believe owners should adopt an open door policy.

• Future work will be continued in the following four areas:



- Continue the study of vulnerable area identification based on the social network theory but using Petri Net to support a more quantitative analysis of interdependency.

- Study the relationship between owners of a CI and governments using modeling and simulation techniques.

- Develop a prototype system for a test bed upon which policies can be studied based on modeling and simulation.

- Study resiliency of a networked CI system.


Question: That is the first time I've seen a ranking of critical infrastructures. Most governments prefer not to rank for obvious reasons. Has that been imbedded? • Yes.

Question: Can you elaborate more about the ranking? I am surprised that the water system has been attributed less importance than food or electricity. • Your intuition is shared by many people. It's a difficult point and it involves some

subjective judgement. • The ranking that we performed is based on the social network theory. We performed

the analysis, applied the information and that is the outcome that resulted. The factors used by the government varied from the ones used through the application of the social network theory.

Question: In reference to the tensions between CI owners and government, is it your understanding that the position from the private sector is the concern about providing specific asset information that opens up vulnerability?

• Yes. There are questions that tmderlie this problem. What type of information and what levels of detail does the owner of CI system need to retain in order to maintain the security and competitiveness of the system?

• In some cases, you ask for information, they say no, and then you find it on the website and it turns out not to have an impact on competitiveness. It warrants further study.

• Additional work is also required to study the trade offs between information sharing, the invasion of privacy, societal freedoms and competitiveness.



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3.6 IMPROVING THE RESILIENCE OF WATER INFRASTRUCTURE AND HEALTHRESPONSE SYSTEMS AGAINST WATERBORNE DISEASES

Edward McBean and Corinne Schuster, University of Guelph

Overview of the Project• The objectives of the project are two-fold:

- To understand potential failure modes and cascade implications to waterinfrastructure and health response systems; and

- To identify strategies of response for Canadian municipalities in order to (a)minimize the failure potential within an individual element of the infrastructure,and (b) ameliorate the cascade effect in the infrastructure systems.

• The project involves a number of partners, without whom this would not be possible.Partners involved in the research include municipalities, who represent the eventualusers of the research, as well as provincial and federal government organizations thatprovide the regulatory framework for the issues that this project is addressing.

Water Management in Ontario• The water management system in Ontario is an aging infrastructure and pipe breaks

regularly happen in many different locations across the province.• The most recent example of illness related to potable water occurred in Kashechewan.

In this case the key problems were poor design, poor training of operators and anemergency back-up system that didn't work because it was never connected.

• Causative factors can include heavy rainfall, drought, flood, Spring runoff andsnowmelt. These all have an impact on the water treatment system which in turn isconnected to other infrastructures.

• An infrastructure cascade sequence can result due to the interdependencies acrosssystems.

Risk Assessment of Drinking Water Supply System Failures in Canada• The objective of the risk assessment is to reduce potential risk in Canadian water

systems and assure safe water. This can be done in two ways:- identify major risk factors of municipal drinking water systems in Canada; and- examine how failure cascade occurs and potential responses.

• In order to identify the potential failure mechanisms within the system, a probabilisticrisk assessment is used.

• This type of assessment requires knowledge of the frequency of each failure item.Unfortunately, not enough data is available and probabilities are hard to assign. Inorder to identify the various risk factors that can have an impact on the waterinfrastructure systems in Canada, fuzzy logic is used to assign linguistic variables.

• The three groupings of variables have been identified that can influence drinkingwater supply failure: (1) problems with the source water, (2) failures in the treatmentsystem, and (3) failures in the distribution system. These form the basis by whichresults are derived.


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• As an example, the failures at Walkerton were as a result of a series of events — flood, bacterial contamination of the well, recovery and lack of treatment and zero or low chlorine.

• The risk assessment analysis provides a qualitative scale for risk. It identifies the potential failures and the impending risks. It is a tool that will allow municipalities to go through a series of steps to analyze the various risk factors associated with the quality of the drinking water.

Chlorine Decay Modeling and Contamination Identification Strategies for Water Distribution Systems

• The objectives of this component of the project are to study chlorine decay in the bulk phase and wall phase and to develop contamination identification strategies using a sensor placement optimization algorithm and a contamination identification algorithm.

• The project is trying to monitor water quality through a sensor system. The key question is where measurements should be taken. The algorithms are designed to locate the best sensor area.

• A field study was conducted on the Goderich water distribution system. Fluoride was shut off for 3 days to produce a low fluoride front and turned-on on the same day of the field study to produce a high fluoride front. Three fire hydrants and one tap on transmission mains and two taps on the representative local mains were selected to collect water samples. Water samples were taken from each location for 9 hours and both bulk and wall decay was analyzed.

• Water distribution systems are spatially diverse and inherently vulnerable to accidental or deliberate physical, chemical or biological threats. Efficient water quality monitoring is an important tool to guarantee a reliable water supply.

• With a limited ability to place sensors into the system, decisions need to be made to minimize detection time, minimize the affected population prior to detection and maximize the detection probability.

• A case study was conducted in the Greater Toronto Area (GTA) to analyze pipe break probabilities and exposure hazards. The GTA has an aging water infrastructure and experiences approximately 1,600 water main breakages per year. An economic cost analysis was conducted that compared economic investment in asset management to the costs of responding to pipe breakages.

• The consequences of pipe breakages are numerous and can be costly: —Contaminant incursion into the drinking water supply system can be ingested,

inhaled and absorbed by consumers. —A significant loss of water can occur. Environment Canada estimates that 13%

of municipal piped water is lost in distribution system leaks and this value is as high as 30% in some communities.

—Traffic hazards are created. —Economic and social disruption can be extensive. —Repair costs can be significant.



Mapping Communication and Learning in Ontario Water Management • The project not only looks at the technical/physical system but also the human

dimension. • Stakeholder communication and learning processes have been reviewed as well as

network structures and the possibilities for collaboration. • Many stakeholders are explicitly laid out in the drinking water legislation; however,

there are several additional players that play less obvious, but important roles such as the mayor and the daycare. Relevant stakeholders that have neither individual nor technical representation with the system, such as the public and the private sector, tend to be unorganized and underrepresented. Unless their participation is solicited and facilitated there will be chronic gaps in effective learning and communication in the drinking water sector.

• Research has shown that communication is not just a public press release. It involves managing relationships, sharing knowledge and communicating outside of the emergency situation.

• The weakest link is everyone's concern. This can be offset through stakeholder communication, redundancy and backup.

• There is a need to challenge the status quo by engaging in active learning, active remediation, cross-sector collaboration on a routine basis, and by facilitating leadership.

• Communication and learning is also premised on valuing existing strengths. This can be done by recognizing and encouraging informal relationships, valuing knowledge, and appreciating the role that relationships play in providing a more secure network in high stress situations. These aspects can be difficult to quantify and qualify but they are fundamental to security.

• Experience has also shown that experts are often not in a position to directly influence decision makers (policy makers, local politicians and citizens). The language used, the networks and the forums appear to be isolated. Consultants seem to be in a better position than owners and operators to drive change.

Current Research: Public Health Intervention • A model of progression and control of a waterborne disease outbreak is being

generated. • The objective is to highlight the impacts, such as burden of illness, as well as to

provide a framework from which to address failures. • The goal is to identify response strategies for Canadian municipalities when dealing

with a waterborne outbreak situation.


Question: You were talking about contamination through pipes. Will you be looking at watersheds and where the water is derived from? • What we're looking at is source water as it arrives at water intake and then its

movement through the system. There's a connection with where the materials came from but it's beyond what we're looking at. It's there but in a less formal link.



• Our biggest hope is (1) to develop a model that can provide structure to municipalities in order to identify concerns, and (2) show the complicated but informative linkages and look at how to strengthen them.

• The emphasis will be on what will likely fail as opposed to the source itself (although we realize it's important and relevant). It is when the succession happens that the problems begin to occur. We don't want to get a lot of false positives that will adversely affect the public.

Question: Do you do any work on the oversight or advice on the overall management system for the province to ensure municipalities are doing what they're supposed to be doing? • There are some recommendations that we are presuming that will corne out of our

research but we are not ready to address that issue right now. • We have a wish list of follow on resources but we haven't moved that far at this point • The regulation outlines the responsibilities and then the question is whether it is being

done and how well is it being done. • We are looking at how provincial regulations are enacted, but not the regulations

themselves.



4. General Discussion Period

At the conclusion of the presentations an open question and answer period was held. Researchers asked the audience to provide feedback on the potential uses of the products that are being developed as each team moves into the final stage of their respective projects. Requests were also solicited for a frank critique of what was heard during the Symposium to help focus their efforts or to encourage researchers to move in certain directions. Listed below are the key points that were discussed during this session.

Application of the Research to Companies and Decision Makers • The challenge is how to take this information back to the workplace and apply it.

We're dealing with a complex area. • As a practitioner, I recognize that there is a global security situation at hand, but as an

individual company we need to know how to be prepared. We need to be able to make assumptions about other infrastructures.

• We still don't have the tools to work through interdependencies across industries. • I agree that the emphasis to date needs to be on modeling the physical

interdependencies, but the human dimension in decision-making and its impact on interdependencies is critical as well. More work is needed in that area.

Identification of Indicators • One area of interest is a specific identification of indicators. There is a lot of data

available but it's still difficult to convince management. • Any improvement toward better defining indicators would be welcome. • Questions also arise as to how to best capture and present this information. The

indicators must be presented in a way that is meaningful. Decision makers like to process simple things and that is how the data needs to be packaged and presented.

Point of Collapse • What has been useful in all of the models is the cascade point — the point of collapse. • The point in simulations where a situation goes from being a manageable problem

and quickly becomes unmanageable. • How much time is there before the point of collapse has been reached? This is a key

component that decision makers want to understand so that they can adjust accordingly.

Building Relationships with Partners • Creating a relationship with partners at various levels of government and across

industries is one of the most important aspects of this type of research. • There is a need to demonstrate the value of this type of research in order to generate

the information that is needed to build models and simulations. • Many private sectors organizations recognize that at certain levels a threshold is

reached and coordination at a massive level is needed. • In reality, by the time that point is reached and a crisis is on hand it is too late.


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• This is a good opportunity for PS Canada to initiate proactive thinking in this area.The dialogue really makes a difference and that can be built on for futureopportunities to work together.

Requests from the Researchers• The question was posed, "Other than money, what do the researchers want from us?"• The unequivocal answer was data. The more industry is willing to collaborate with

the research teams the faster the outcomes of the research will diffuse and be appliedacross industries and across all levels of government.

• An additional request is for industry and government to continue to provide learningopportunities and to create jobs in these areas. New students are being trained in theseareas and are at the cutting edge of this field of research. These projects can go fromresearch to implementation in a very short period of time. Money and lives can besaved. Researchers want to know that if they train people in these areas there will bejobs for them and they will have the capacity to develop useful products.

Funding• This remains a strategic priority for NSERC.• Although the mechanism may change, funding will be made available for additional

projects.


Documents

Archived Content Contenu archivé 551.5.c2 s965 2007-eng.pdf · • The simulator can model the effect of decisions made by emergency management command and control and different