Developing Early Risk Detection and Preparedness System with Risk Analysis and Contingency Plan

PresentationWhen the natural or human-made disasters, such as hurricanes, floods, tornadoes, wildfires and gas leaks, threaten a populated area, mass casualties and property losses may be followed. To avoid, minimize or eliminate the risks for public safety, a well-organized early risk detection and preparedness system is needed in order to save lives and minimize losses. To make this early detection system efficient yet effective, a mobile app, risk preparedness aid, was developed. This aid system can communicate with sensors, location information, and disaster management server. The aid was designed using the concepts of location based service and risk management and it includes gas leak detection, warning and emergency evacuation procedure with routing. Based on the identified risks and preparing procedure, various contingency plans were developed. The contingency plans should be very clear so that it is easy for public and employee to follow. Because each system has unique infrastructure its contingency plan must be unique. This paper also shows an evacuation process in the form of a flowchart for ease of use in the event of an emergency

A system dynamics & emergency logistics model for post-disaster relief operations

Emergency teams’ efficiency in responding to disasters is critical in saving lives, reducing suffering, and for damage control. Quality standards for emergency response systems are based on government policies, resources, training, and team readiness and flexibility. This research investigates these matters in regards to Saudi emergency responses to floods in Jeddah in 2009 and again in 2011. The study is relevant to countries who are building emergency response capacity for their populations: analysing the effects of the disaster, communications and data flows for stakeholders, achieving and securing access, finding and rescuing victims, setting up field triage sites, evacuation, and refuges. The research problem in this case was to develop a dynamic systems model capable of managing real time data to allow a team or a decision-maker to optimise their particular response within a rapidly changing situation. The Emergency Logistics Centre capability model responds to this problem by providing a set of nodes relevant to each responsibility centre (Civil Defence, regional/local authority including rescue teams, police and clean-up teams, Red Crescent). These nodes facilitate information on resource use and replenishment, and barriers such as access and weather can be controlled for in the model. The dynamic systems approach builds model capacity and transparency, allowing emergency response decision-makers access to updated instructions and decisions that may affect their capacities. After the event, coordinators and researchers can review data and actions for policy change, resource control, training and communications. In this way, knowledge from the experiences of members of the network is not lost for future position occupants in the emergency response network. The conclusion for this research is that the Saudi emergency response framework is now sufficiently robust to respond to a large scale crisis, such as may occur during the hajj with its three million pilgrims. Researchers are recommended to test their emergency response systems using the Emergency Logistics Centre model, if only to encourage rethinking and flexibility of perhaps stale or formulaic responses from staff. This may lead to benefits in identification of policy change, training, or more appropriate pathways for response teams