This document discusses early warning systems and provides definitions from various domains. It notes that there is no universally accepted definition of an early warning system. Descriptions of early warning systems include monitoring systems designed to detect problems in drinking water quality and strategic systems to support proactive business management. The document also discusses a prototype meta-systemic early warning system called SCEWA being developed to monitor water treatment plants in Ireland and support risk management.

1. Early Warning Systems
and
Systems Safety
Dr. Ioannis M. Dokas
Cork Constraint Computation Centre
University College Cork

3. EWS: The Definition Problem
• A universally accepted definition of an early
warning system does not yet exist. Probably
one never will.
(Source: http://ccb.colorado.edu/warning/report.html )

4. Some Facts on EWS
• Many descriptions / definitions
• There is a great variety of designs – development
approaches
• In many domains
– Energy
– Medicine
– Currency crises
– Military
– Crisis Management
– Environment

5. Some Facts on EWS

6. Resembling Concepts
• Many resembling concepts
– EW models
– EW indicators
– Accident precursors

7. Why This Trent?
• The need of being proactive to accidents and
disasters is getting bigger
• Better tools allow us to imagine that it is
feasible to prevent accidents and better adapt
to disasters

8. Types of Definitions
• Focused on:
– Aim
– How EWS are used in practice
– Functions
– Components

9. Domain: Business Intelligence
• Strategic EWS
• The aim of a competitive EWS is to support
the proactive strategic management of the
business. It is composed of an iterative three
part approach that starts with the Risk
Identification, continues with Risk Monitoring
and ends with the Management Action

10. Domain: Drinking Water
• EWS is an integrated system for monitoring
analyzing interpreting and communicating
monitoring data, which can then be used to
make decisions that are protective of public
health and minimize unnecessary concern and
inconvenience to the public
• Technologies and Techniques for Early Warning Systems to Monitor and Evaluate Drinking Water Quality,
US EPA

11. Domain: Drinking Water
• EWS are used to detect any sudden
deterioration in the quality of the source
drinking water supply either just before the
water goes into the distribution system or
some distance upstream.
• International Life Science Institute (Brosnan 1999)

12. Domain: Drinking Water
• An ideal EWS
– (1) exhibits warning in sufficient time for action,
– (2) provides affordable cost,
– (3) requires low skill and training,
– (4) covers all potential threats,
– (5) identifies the source,
– (6) demonstrates sensitivity to quality changes at regulatory levels,
– (7) gives minimal false positive or negative responses ,
– ( 8) exhibits robustness,
– (9) allows remote operation, and
– (10) functions year-round.
• International Life Science Institute (Brosnan 1999)

13. Dictionary Definition
• A system or procedure designed to warn of a
potential or an impending problem.
– Note: The only action is to warn

14. UN Framework for EWS
(Natural Hazards)
Source : UN - ISDR
Third International Conference on Early Warning 27-29 March 2006 Bonn, Germany

15. EWS = Process Control Loop

16. • Are Sensors EWS?
• Are EW indicators EWS?

17. Perceptions of EWS
• A
• B
• C

18. Alert Systems vs EWS
• Feedback :
• Comparison between
actual and target values
(Alert Systems)
• Feedforward:
• Detection of possible
disturbances coming
from the environment
(e.g. EWS for Natural
Phenomena)
• Detection of possible
disturbances or
precondition of failures
coming from the
controlled process
(Metasystemic control
and EWS)

19. Disturbances Coming From the Environment
• http://www.hewsweb.org/hp/

20. Proactive Metasystemic Control
• Need to “enter” in to the lower hierarchical
levels of the controlled process
• Identify the feedback control loops which
form the controlled process
• Define how the feedback control loops can fail

21. Proactive Metasystemic Control
• Level 0
• Level 1

22. Metasystemic EWS
• Example: EWS for Drinking Water Treatment
Plants in the Republic of Ireland
• (Brief description will be given at the end of the
presentation)

23. • BUT!!! One Moment Please
• What Metasystemic realy means?

24. • ORGANISATIONS

25. Organizations
• Organizations = complex systems
– A collection of hierarchical structured feedback
loops
• Interact with the environment
• To accomplish a purpose (or a hierarchy of
purposes)
• Top purpose: Maintain existence
• Adapt and evolve

26. Cybernetics
• The science of control and communication in
complex, dynamical systems (Wiener, 1948)
• The science of the emergence and design of
order (Malik, 2001)
• The science of effective organization (S. Beer,
1974)

27. Complexity
• Structural: Number of components in a system
or the number of combinations one must
consider in making decisions.
• Dynamic: Arise from the interactions among
agents in time. (Sterman, 2000)

28. Emergence
• Emergent properties are properties of the
‘whole’ not possessed by any of the individual
parts making up this whole.
• Example: Safety

29. Viability
• Viability = The ability to maintain a separate
existence (Beer, 1979)
• An organization should aim at viability beyond
survival – i.e., a viability which transcends
mere maintenance of a given identity
(Schwaninger 1993, 2001b)

30. Variety
• Variety = Measure of Complexity
• The number of different states or modes of
behaviour a certain system can adopt
(Schwaninger, 2006)

31. Elements of a Viable System
• Operations
• Management / Metasystem

32. Law of Requisite Variety (R. Ashby)
• Only variety can destroy/absorb variety
Reality: Ve > Vo > Vm
Ideally: Ve = Vo = Vm
Basic Elements of the VSM model (S. Beer)

33. Principals of Organization (S. Beer)
Managerial, operational and environmental varieties diffusing through an institutional
system, tend to equate; they should be designed to do so with minimum damage to
people and cost.
The four directional channels carrying information between the management unit, the
operation, and the environment must each have a higher capacity to transmit a given
amount of information relevant to variety selection in a given time than the originating
subsystem has to generate it in that time.
Wherever the information carried on a channel capable of distinguishing a given variety
crosses a boundary, it undergoes transduction; the variety of the transducer must be at
least equivalent to the variety of the channel.

34. Elements of a
Viable System
MetaSystem
• S1 – Implementation
• S2 – Co-ordination
• S3 – Internal Control
– S3* Audit
• S4 – Intelligence and
Development
• S5 – Strategy – Policy -
Ethos

35. EWS in Organizations
• 3 Types
–Strategic
–Operational
–Meta-systemic

36. EWS In Organizations
• “Hard” and “Soft” EWS
– Coherence (Hitchins, 2007)
• A soft system does not have a clear, singular purpose:
instead, it may have many, conflicting purposes,lack
synergy, etc.
• A hard system would have a clear, singular purpose,
and would have all the parts within that system
contributing towards that singular purpose
– Technology (Hitchins, 2007)
• ‘soft’ and ‘hard’ refer not to the coherence of the
system in question, but to the predominance or
otherwise of technology in the system.

37. Metasystemic EWS Do Exist!
• Have the form of safety procedures - periodic
reports - internal regulations
• Existing Metasystemic EWS = Soft EWS
• However. There are not any hard
metasystemic EWS

38. • Types of Problems

39. AXIOM
NO PROBLEM – NO EWS
P = Si - Sr

40. Causal Factors of Problems
• External
• Internal

41. TYPES OF PROBLEMS
S. French et al. (2009) Decision behaviour, analysis and support

42. The Cynefin Framework
• A sense making framework that helps to
categorise problems based on the nature of
the relationship between cause and effect into
five contexts.
http://www.youtube.com/watch?v=N7oz366X0-8

43. “Performance Meter” of EWS

44. Domain: Drinking Water
• An ideal EWS
– (1) exhibits warning in sufficient time for action,
– (2) provides affordable cost,
– (3) requires low skill and training,
– (4) covers all potential threats,
– (5) identifies the source,
– (6) demonstrates sensitivity to quality changes at regulatory levels,
– (7) gives minimal false positive or negative responses ,
– ( 8) exhibits robustness,
– (9) allows remote operation, and
– (10) functions year-round.
• International Life Science Institute (Brosnan 1999)

45. Early Warning
• The expression ‘early warning’ is used in many fields
to mean the provision of information on an emerging
dangerous circumstance where that information can
enable action in advance to reduce the risks involved
(Basher, 2006 Phil. Trans. R. Soc. 364, 2167–2182 doi:10.1098/rsta.2006.1819)

46. Signal – Sign - Alert
• Signal: It needs a transmitter (Measurable – A
strong signal)
• Alert: A verified event which denotes that a
“system level hazard” has occurred
• Sound signal vs Weak Signal

47. Types of Signals
• Those that are beyond our perception
• Those that are within our perception but
unrecognised by our mental models
• Signals recognised by our mental models that
we use to modify our behaviour.
Bryan Coffman, “Weak Signal Research”
http://www.mgtaylor.com/mgtaylor/jotm/winter97/wsrintro.htm

48. Weak Signal
• A development about which only partial
information is available at the moment when the
response must be launched, if it is to be
completed before the development impacts on
the firm. (Ansoff, 1984)
• A weak signal is a factor for change hardly
perceptible at present but which will constitute a
strong trend in the future (Michelle Codet).

49. Filters of Weak Signals (I. Ansoff)
• A weak signal has to pass three different filters
to have an impact
• Strategic EWS

50. EWS Justification Model

51. Causal Factors

52. • Safety of Systems / Organisations

53. Safety
• Safety is an emergent property of systems that
arises when system components interact with
each other within a larger environment (Leveson)
• Safety is a control problem (Leveson, Rasmusen)
• Safety is a dynamic non event (Weik)
– a stable outcome produced by constant adjustments
to system parameters. To achieve stability, change in
one system parameter must be compensated for by
changes in other parameters, through a process of
continuous mutual adjustment.

54. Hazards and Accident
• Hazard: a state or set of conditions of the
system that together with other conditions in
the environment will lead to an accident
• Accident: undesired and unplanned events that
result in a loss

55. Accident Models
• Provide descriptions of the conceptual
elements needed to explain the phenomenon
of accidents.
– sequential,
– epidemiological and
– systemic

56. Sequential
• The sequential models
explain accidents as the
result of a sequence of
“root cause” events
• Social or historical
background of an individual
 drive individual to make
an error  leads to an
unsafe act or condition
leads to an accident and
an injury. http://www.ekdrm.net/e5783/e17327/e24075/e27357/

57. Common Types of Events
• Component failures, human error, or energy-
related event
• The basic accident model for common hazard
analysis
– FTA, FMECA, Event Trees, etc.

58. Limitations of Hazard Analysis Based
on the Sequential Model
• Social Factors
• Organizational factors
• Software
• Human error
• Adaptation

59. Epidemiological
• The epidemiological models explain accidents
with a set of factors, some of which are
obvious and some are latent.

60. Systemic
• The systemic models view accidents as the
result of dysfunctional and in some cases
unexpected interactions between system
components.

61. • A Prototype Metasystemic EWS

62. SCEWA Project
• A 5 year research project (800K Euros)
• Begun January 2008
• Goal: To design and develop a prototype web
based early warning system for water
treatment plants
• Aim: To support a Proactive Risk Management
Strategy

63. Drinking Water Quality in Ireland
• Failures in meeting drinking
water standards
• Boil water notices
• Sever consequences
– More than 200 lab-tested cases
of cryptosporidiosis in Galway
• A third of all public water
supplies in Ireland are
vulnerable (EPA report)

64. Drinking Water Safety
• “Safe water” means that potential harmful
substances, depending on their nature and
characteristics, are either absent from the
water or their quantities falls below safety
standards
• Standards are updated periodically

65. The Role of EWS

66. Safety: The Basic Concept
• Knowledge of how accidents occur
• From which threats a system must be protected
from
• Safety is considered as emergent property of the
system (interaction among components may
produce hazardous behaviours that are
previously unidentified)
• Monitoring of hazards (physical, chemical,
microbial, radiological agents) only is not enough

67. Approaches for Safe Drinking Water
• Multiple Barrier Approach
• Water Safety Plans
• Hazard Analysis and Critical Control Points
Monitoring and Control
Raw Water Drinking Water

68. The Socio-Technical System
POLICIES,
STRATEGIC DECISIONS,
CONTROL MECHANISMS
HUMAN ACTIVITIES

69. Stakeholders

70. Use Case
PROACTIVE
SYSTEM
WARNING
LA SLIGO WTP
SLIGO
WWW
HSE
EPA

71. Selected Methods and Technologies
• Domain Specific Modelling
• Software as a Service
• Bayesian Belief Networks
• Hidden Markov Models (under development)

72. Domain Specific Modelling Language
• Users develop models using a graphical language
which has specific syntax and semantics
• Based on the graphical models executable code is
generated

73. Example
Water Service Authority
Hazard
Analyst State Agency

74. Understanding the Domain
• IDEFØ model (Integration Definition for Function Modeling)
74

75. Meta-model
•Eclipse EMF Ecore to perform metamodeling
•Java Persistence API (JPA) annotation for object-
relational mapping approach.
75

76. The Editor
M2T transformation using XPand
• The code is executed with the SMILE BBN
engine
76

77. Technologies
• Eclipse’ GMF has been adopted to build the core
architecture,
• Which consists of two frameworks:
• For Metametamodeling Model-based Eclipse Modeling
Framework (EMF) technology based upon a subset of
the Object Management Group standard (OMG).
• Graphical Editing Framework for graphical editor
creation.
• Other Technologies used are UML2 Tools, OCL, XML
Schema definition
• To provide persistency we have used Teneo, Hibernate.

78. Code Generation
• For code generation openArchitectureWare platform
is integrated in which M2T transformation is
performed using Xpand.
• Further Technologies to be integrated
• PostgreSQL
• Apache Tomcat
• Eclipse Rich Client Platform (RCP)
• Eclipse Rich Ajax Platform (RAP)

79. A SaaS Approach for Socio-technical EWS
•Multi-users scattered all over the country
•Users run the software using a Web browser
•No extra hardware, software nor plug-in
•No upfront license fees required! Pay as you go!
•Easy to update
•Leverage the economy of scale Cost Efficient

80. SaaS Details
• Several Tenants:
– Water Service Authorities
– WTP personnel
– Health Service Executive (HSE)
– Environmental Protection Agency (EPA)
– Drinking Water Laboratories
• User inputs and sensor data are considered as
evidence for the BBNs (SMILE Engine)
• The BBN result represents our updated belief
about the occurrence of a system hazard in
each WTP

81. Technologies Used
• Linux, Apache, MySQL, PHP and PostgreSQL.
• PHP 5.2 was used as the server scripting language
while Apache 2.2 was our Web
• PostgreSQL 8.3 because provides a native support for
XML and a build-in query mechanism based on Xpath
1.0.
• Postgre SQL 8.3 exports the result of a query to an
XML document and check the well-formedness of an
XML document such as XMLPARSE and
XMLSERIALIZE.

82. Expert Catalogue

83. Definition of a WTP

84. Status Update by Auditors

85. State Agencies View

86. Laboratory View

87. Hazard Level Estimation
(Accessible in all views)

88. Metasystemic EWS
• “Typical EWS” provide inputs
• Users provide inputs (e.g. Audit reports, Warning
signals, Change of working conditions)
• Monitoring for the concurrency of signals/events
indicating shift from a safe system state
• The mechanism detecting the deterioration of
safety is based on Systemic Accident models

89. Metasystemic EWS
• The output is not a forecast
• It raises a flag (warnings) when deterioration
of safety has been detected
• The stakeholders who form the governance
model of safety in the system are “tenants” of
the socio-technical EWS
• A socio-technical EWS is a socio-technical
system (it may fail, like the reference system,
due to the same general processes)

90. Thank you
Dr. Ioannis M. Dokas
e-mail: i.dokas@4c.ucc.ie