Leorey Marquez, Pawan Gamage, Dhirendra Singh, Vincent Lemiale, Trevor Dess, Peter Ashton, et al. (2023). SEEKER: A Web-Based Simulation Tool for Planning Community Evacuations. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 8–24). Palmerston North, New Zealand: Massey Unversity.
Abstract: Bushfires cause widespread devastation in Australia, one of the most fire-prone countries on earth. Bushfire seasons are also becoming longer and outbreaks of severe bushfires are occurring more often. This creates the problem of having more people at risk in very diverse areas resulting in more difficult mass evacuations over time. The Barwon Otway region in Victoria’s Surf Coast Shire is one such area with evacuation challenges due to its limited routes in and out of coastal areas and its massive population surges during the tourist season and holiday periods. The increasing gravity of the bushfire threat to the region has brought about the Great Ocean Road Decision Support System (GOR-DSS) project, and the subsequent development of a disaster evacuation tool to support emergency management organisations assess evacuation and risk mitigation options. This paper describes the design and development of SEEKER (Simulations of Emergency Evacuations for Knowledge, Education and Response). The SEEKER tool adds another level of intelligence to the evacuation response by incorporating agent-based modelling and allows emergency management agencies to design and run evacuation scenarios and analyse the risk posed by the fire to the population and road network. Furthermore, SEEKER can be used to develop multiple evacuation scenarios to investigate and compare the effectiveness of each emergency evacuation plan. This paper also discusses the application of SEEKER in a case study, community engagement, and training.
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Tomasz Opach, Carlo Navarra, Jan Ketil Rød, & Tina - Simone Neset. (2020). Towards a Route Planner Supporting Pedestrian Navigation in Hazard Exposed Urban Areas. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 517–528). Blacksburg, VA (USA): Virginia Tech.
Abstract: This study aims to design a route planner functionality that includes real-time context information from physical sensors and citizen observations to support pedestrian navigation in urban areas exposed to extreme heat and floods. Urban population is growing and people living in urban areas are especially exposed to heat and urban flooding, which are two of the anticipated effects of climate change. Route planning functionality can be of value to individual citizens, especially those with limited mobility, as well as for healthcare professionals and authorities who are responsible for crisis response and management. Although the route planner functionality is to be experimentally implemented in a specific tool with the use of broadly available web technologies and real time data, a major generic outcome is the framework that can be used to develop the functionality as part of a decision support tool of any kind.
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Yaniv Mordecai, & Boris Kantsepolsky. (2018). Intelligent Utilization of Dashboards in Emergency Management. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1108–1119). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Effective decision-supporting visualization is critical for strategic, tactic, and operational management before and during a large-scale climate or extreme weather emergency. Most emergency management applications traditionally consist of map-based event and object visualization and management, which is necessary for operations, but has small contribution to decision makers. At the same time, analytical models and simulations that usually enable prediction and situation evaluation are often analyst-oriented and detached from the operational command and control system. Nevertheless, emergencies tend to generate unpredictable effects, which may require new decision-support tools in real-time, based on alternative data sources or data streams. In this paper, we advocate the use of dashboards for emergency management, but more importantly, we propose an intelligent mechanism to support effective and efficient utilization of data and information for decision-making via flexible deployment and visualization of data streams and metric displays. We employ this framework in the H2020 beAWARE project that aims to develop and demonstrate an innovative framework for enhanced decision support and management services in extreme weather climate events.
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Fatemeh Hendijani Fard, Cooper Davies, & Frank Mauer. (2017). Agile Emergency Responses Using Collaborative Planning HTN. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 857–867). Albi, France: Iscram.
Abstract: Emergency response planning is a complex task due to multiple organizations involved, different planning considerations, etc. Using artificial intelligence collaborative planning helps in the automatic planning for complex situations. Analyzing all impacting factors along with plans that are executable can facilitate the decision making in Emergency Operations Centers for an agile emergency response. A main component of a planner is a knowledge base. Although many systems are developed to support decision making in emergency response or recovery, they either focus on specific or small organizations, or rely on simulations. To the best of our knowledge, there is a gap that there is no common knowledge base for provincial level mass emergencies for automatic planners. The multiplicity of the emergency response documents and their structure makes the knowledge acquisition complex. In this paper, we explain the process of extracting knowledge based on hierarchical task networks and how it speeds up the reactivity to a disaster.
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Tanaporn Panrungsri, & Esther Sangiamkul. (2017). Business Intelligence Model for Disaster Management: A Case Study in Phuket, Thailand. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 727–738). Albi, France: Iscram.
Abstract: This research presents the conceptual Business Intelligence (BI) model for disaster management. BI can provide agility capacity for decision making in dynamic environment among different agencies. This project designs and develop a data warehouse using multi-dimensional model for severity analysis of flood and landslide in risk area using case study from Department of disaster prevention and mitigation (DDMP), Phuket, Thailand. The concept of BI can be applied for extremely heterogeneous data structures and data platform environment to improve data quality and expose to better decision-making for disaster management. In the next stage of this project, we will integrate more data sources from other agencies for example GIS data from Phuket land-use planning and flooding prediction model database. The result of this study will help organization deploy BI more effectively.
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