The Factors Affecting Institutional Performance in Disaster Management in Oman, Suad Saud AL MANJI

6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
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Title of your presentation
List of authors, organisation, country

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Introduction
• Improved institutional arrangements that build sustainable
resilience and development can be achieved by linking the
socio-economic system with potential extremes in the natural
system (Balica et al. 2012; Pearce 2002; UN/ISDR 2004).
• Oman is vulnerable to natural disasters, such as cyclones
(Membery 2001,2002), and is in the process of enhancing
resilience to improve safety during extreme weather events.
• Identifying the factors affecting efficient disaster management
is important for improving the response.
• A fuzzy cognitive mapping (FCM) approach was used
visualise and reduce the complexity of studying the system
(Gray et al., 2011).

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Content
• Fuzzy Cognitive Map
• Data Collection
• Result
• Discussion
• conclusion

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Fuzzy cognitive Map
• FCM is a participatory qualitative approach used to represent
stakeholder knowledge or perception of the performance of a complex
system through cognitive thinking (Kontogianni et al., 2012; Michael,
2009).
• The technique was first proposed by Kosko (1986) as a means to
develop cognitive maps using fuzzy logic.
• It has been widely used to indicate the strength of relationships
between variables within the system, and to understand the function of
the system (Carvalho, 2013).
• example : Olazabal & Pascual (2015) assessed the possible use of
FCM modelling to simulate complex social, economic and political
systems.

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Fuzzy cognitive maps structures and functions
C1
C2
C5
C3
C4
C6
E1,5
E1,3
E1,4
E5,3
E3,5
E3,2
E6,4
E6,2
E4,6
Component
Transmitters variables with significant
impact in the system.
positive out-degree and
no in-degree value
Receivers variables that are strongly
influenced by out-side
variables and have no impact
in the system (Gray et al.,
2011; Abbas, 2014).
Positive in-degree and no
out-degree
ordinary Variables in between positive in-degree and
positive out-degree.
Out degree the cumulative strength of the connection out of the
variables
In degree the cumulative strength of the connection entering the
variables.

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Fuzzy cognitive maps structures and functions
Function
Density
(index of
connectivity)
D= C/N2
or D= C/[N(N-1)](Gray et al. 2011,
Hage & Harary, 1983).
number of concepts (N)
number of connections (C)
indicates whether the concepts in the system
are well connected (i.e. a democratic system) or
if some components are more influential in the
system (i.e. a hierarchical system) (Gray et al.,
2011; Özesmi & Özesmi, 2004)
complexity determined by the centrality, which is the
ratio of receiver variables to transmitter
variables (R:T) (Gray et al., 2011).
An FCM with a high number of receivers has a
more complex map and reflects outcome
components (Gray et al., 2011; Eden et al.,
1979). Conversely, the maps with higher
number of transmitter variables indicate top-
down thinking and represent maps with more
forcing functions (Gray et al., 2011; Eden et al.,
1979).
The amount of potential change and level of complexity are determined by calculating
the density and complexity of each FCM (Gray et al., 2011; Abbas, 2014).

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Data collection
• FCM workshop in Oman
• The participants were stakeholders from different government and
non-governmental disaster management organisations.
• They were asked to identify the factors affecting the organisational
and institutional resilience to extreme weather events from the five
thematic areas provided by Hyogo Frame Work (UN/ISDR, 2005):
governance; risk assessment; knowledge and education; risk
management and vulnerability reduction; and disaster preparedness
and response.
• The participants drew cognitive maps to show the relationship
between the factors. Finally, the participants provided a quantitative
value for the relationship between the factors with -1 for a strong
negative and +1 for a strong positive relationship.

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Result and discussion
• Four cognitive maps were collected from the workshop.
• Table 1 summarises the structural and functional measurements in each FCM.
Community Disaster Institutions Management
Number of variables 33 20 25 22
Number of connections 42 36 47 47
Number of transmitters 9 5 7 6
Number of receivers 8 2 0 0
Number of ordinary 9 13 18 16
C/N 1.273 1.8 1.88 2.136
Centrality (R:T) 0.89 0.4 0 0
Density 0.039 0.9 0.075 0.097

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Transmitter (9) out-degree Centrality
Water availability 1.00 1.00
Coordination between
institutions (government &
non-government) 1.25 1.25
Communication quality 1.50 1.50
Sewage 1.75 1.75
Transport (Roads) 2.00 2.00
Financial support for the
institutions and community 2.00 2.00
Receiver (8) in-degree Centrality
Public awareness about
hazards 1.00 1.00
Bank service (Chash
avilability during the event
) 1.00 1.00
Shelters preparedness 2.50 2.50
Evacuation and relief 3.25 3.25
Community group

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Disaster group
Transmitter (5)
out-degree Centrality
Speed of data sharing
0.50 0.50
central data system
0.50 0.50
Internal institutions training
0.75 0.75
Early warning
1.00 1.00
Financial resources
2.25 2.25
Receiver (2)
in-degree Centrality
Food security
1.75 1.75
Emergency and evacuation
plan 3.25 3.25

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Institutions group
Availability of basic services (
water, communications,
electricity …ect) 0.50 0.50
Volunteering teams 1.50 1.50
Media 2.25 2.25
Central data system 2.50 2.50
Physical resources (equipment) 2.50 2.50
Regulation and illegalisation 4.00 4.00

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Management group
Institutions emergency plans 0.50 0.50
Knowledge of work 1.25 1.25
Absent of strict laws for
misbehavior during the disaster 1.50 1.50
Coordination between institutions
in emergency 1.50 1.50
Luck in Financial support for
infrastructure projects 1.75 1.75
Absent of evaluation of the
committee work 2.00 2.00
Absent of Financial plan for disaster
management 2.25 2.25

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Conclusion
• Identifying the factors that play a significant role in the resilience of
the social system to hazards helps to build the effective policies for
the disaster management.
• In this study FCM was used to (1) compare the structural and
functional differences between stakeholder’s maps; (2) reveal the
factors affecting community resilience to extreme weather events
based on the thematic areas provided by the Hyogo Frame Work.
• The results identify many factors, including governance, risk
assessment and risk management, and public awareness, affecting
disaster management in Oman. Improving these factors will facilitate
building better policies to create a more resilient disaster management
system in the country.

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References
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