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

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6th International Disaster and Risk Conference IDRC 2016 Integrative Risk Management - Towards Resilient Cities. 28 August - 01 September 2016 in Davos, Switzerland

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

  1. 1. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Title of your presentation List of authors, organisation, country
  2. 2. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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).
  3. 3. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Content • Fuzzy Cognitive Map • Data Collection • Result • Discussion • conclusion
  4. 4. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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.
  5. 5. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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.
  6. 6. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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).
  7. 7. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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.
  8. 8. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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
  9. 9. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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
  10. 10. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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
  11. 11. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Institutions group Transmitter (6) out-degree Centrality 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
  12. 12. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Management group Transmitter (6) out-degree Centrality 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
  13. 13. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org 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.
  14. 14. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org References • Abbas, N. H. (2014). The impact of trust relationships on environmental management in North Lebanon. PhD Thesis, Universiteit Twente • Balica, S. F., Wright, N. G. & Meulen, F. (2012). A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards, 64, 73-105. • Carvalho, J. P. (2013). On the semantics and the use of fuzzy cognitive maps and dynamic cognitive maps in social sciences. Fuzzy Sets and Systems, 214, 6-19. • Gray, S., Chan, A., Clark, D. & Jordan, R. (2012). Modeling the integration of stakeholder knowledge in social–ecological decision- making: Benefits and limitations to knowledge diversity. Ecological Modelling, 229, 88-96. • Hage, P. & Harary, F. (1983). Structural models in anthropology. Cambridge University Press. • Jetter, A. J. & Kok, K. (2014). Fuzzy Cognitive Maps for futures studies—A methodological assessment of concepts and methods. Futures, 61, 45-57. • Jones, R. E. T. (2006). The development of an emergency crisis management simulation to assess the impact a fuzzy cognitive map decision-aid has on team cognition and team decision-making. Doctoral dissertation, The Pennsylvania State University. • Kalaugher, E., Bornman, J. F., Clark, A. & Beukes, P. (2013). An integrated biophysical and socio-economic framework for analysis of climate change adaptation strategies: the case of a New Zealand dairy farming system. Environmental Modelling & Software, 39, 176- 187. • Kok, K. & van Vliet, M. (2011). Using a participatory scenario development toolbox: added values and impact on quality of scenarios. Journal of Water and Climate Change, 2(2-3), 87-105. • Kontogianni, A. D., Papageorgiou, E. I. & Tourkolias, C. (2012). How do you perceive environmental change? Fuzzy Cognitive Mapping informing stakeholder analysis for environmental policy making and non-market valuation. Applied Soft Computing, 12(12), 3725-3735. • Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-machine Studies, 24(1), 65-75.
  15. 15. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org • Membery, D. (2001). Monsoon tropical cyclones: Part 1. Weather, 56(12), 431-438. • Membery, D. (2002). Monsoon tropical cyclones: part 2. Weather, 57(7), 246-255. • Meliadou, A., Santoro, F., Nader, M. R., Dagher, M. A., Al Indary, S., & Salloum, B. A. (2012). Prioritising coastal zone management issues through fuzzy cognitive mapping approach. Journal of Environmental Management, 97, 56–68. • Olazabal, M. & Pascual, U. (2016). Use of fuzzy cognitive maps to study urban resilience and transformation. Environmental Innovation and Societal Transitions, 18, 18-40. • Özesmi, U. & Özesmi, S. L. (2004). Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1), 43-64. • Pearce, L. (2002) Disaster Management and Community Planning, and Public Participation- How to Achieve Sustainable Hazard Mitigation. Natural Hazards, 28, 211-228. • Papageorgiou, E. I. & Salmeron, J. L. (2013). A review of fuzzy cognitive maps research during the last decade. Fuzzy Systems, IEEE Transactions on, 21(1), 66-79. • Papageorgiou, E. & Kontogianni, A. (2012). Using fuzzy cognitive mapping in environmental decision making and management: a methodological primer and an application. INTECH Open Access Publisher. • Samarasinghe, S. & Strickert, G. (2013). Mixed-method integration and advances in fuzzy cognitive maps for computational policy simulations for natural hazard mitigation. Environmental Modelling & Software, 39, 188-200. • UN/ISDR. (2004). Land Use, Disaster Risk & Rewards, A community Leader's Guide. In Africa Educational Series, ed. U. I. Africa. Africa: UN. • UN/ISDR. (2005). Hyogo Framework for Action 2005-2015, Building the Resilince of Nations and Communities to Disasters. In World Conference on Disaster Reduction. Japan: UN. • UN/ISDR. (2014). Natural Disasters in the Middle East and North Africa: A Regional Overview. The International Bank for Reconstruction and Development / The World Bank.
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