Abstract: During the earthquake rescue, rapidly locating the trapped person is a critical issue to reduce casualties. Compared with the ground search after the earthquake, the unmanned aerial vehicle (UAV) life detection is not only more expeditious but also safer. For shortening the mission completion time of UAV, we propose the coupling method of Dijkstra's algorithm and simulated annealing (SA) algorithm to optimize the search path. Concisely, the mathematical model is further abstracted as the Traveling Salesman Problem (TSP) and the shortest loop can be obtained by SA algorithm. The real geo-environment of Jiuzhaigou and the actual large-scale rescue scenarios are taken into consideration. Setting six key search areas as our life detection objects, the UAV 3D path simulation is conducted with MATLAB, which achieves the obstacle avoidance. Our UAV path planning method can significantly speed up the search process and save more people in the post-disaster search.

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.

Abstract: In order to cope with increasing complexity of catastrophic disasters, resilience is considered an essential capability of an incident management system (IMS). As resilience is manifested during systems operation, a naturalistic observational study was conducted to understand how resilient performance dynamically takes place in this domain. The study results were presented using the concept of episodes, each of which uncovers a trace of such resilient performance following an information input called an inject. The episode analysis also facilitated the identification of complex and dynamic interactions among human and technological agents to satisfy work demands, representing work-as-done (WAD) in large-scale emergency response operations.

Abstract: Responders and decision makers can be trained through simulation tools where participants learn how to deal with an ongoing crisis and make decisions through a realistic, simulated environment using a game or gamification approach. Training through a simulated, virtual crisis tool would be a more affordable way of conducting a drill, as a supplement to field drills. In this paper, we describe the requirements' elicitation process for co-design of a virtual training tool for emergency management. The cooperative design process included researchers and end-users together to generate potential solutions for a defined problem. The elicitation process involved brain-storming, interviews and a workshop together with representatives from emergency stakeholders. A systematic qualitative data analysis was conducted. The paper reports our analysis results which serve as a basis for further development of an emergency management virtual training tool using an extreme weather scenario.

Abstract: WalkSafe is a location-based app that notifies users of emergencies around them. The app is compared to The Pennsylvania State University's emergency notification system – PSUAlert, which provides time-based alerts. We identify weakness of the existing PSUAlert system and address them by introducing a location-based emergency notification system with the records of past incidents along with the type of emergency with respect to the user's location. We gathered user perception from 43 survey respondents that informed the design of the WalkSafe app. We use mixed-methods approach to evaluate WalkSafe with PSUAlert system as a baseline. We assess both systems with 22 participants by notifying them of the fake emergencies and asking them to use both systems to understand details regarding the emergency and its location. The pre- and post-surveys are evaluated using content analysis and paired t-test. Participant reported higher perceived convenience, perceived security, willingness to use, and willingness to share when using WalkSafe.

Abstract: In complex emergencies, civil and military organizations often find themselves being partners in an international effort aimed at peace keeping, humanitarian relief, and development support. Civil and military partners need to exchange information and to cooperate as required. This assumes effective and efficient Civil-Military Interaction (CMI). However, CMI research literature shows that, in practice, this is far from a reality. In particular, our research indicates that deficiencies in knowledge processes and knowledge management within international civil and military organizations contribute to the causes of ineffective and inefficient CMI. Our research aims to investigate the feasibility of developing technical solutions exploiting knowledge engineering, to support fieldworkers in overcoming these CMI problems. As a first step, this paper introduces a Conceptual Framework (CF) that captures reference models of the CMI domain. The CF has been developed to analyze CMI problems and underlying KM deficiencies. It is being illustrated, explored and validated using real-world case studies.

Abstract: Although one of the dominant paradigms in managing disaster operations is that of modeling decisions around the activities of humanitarian organizations, recent literature has highlighted the importance of managing disaster operations from the perspective of the affected community. Modeling community-driven disaster operations has a unique set of challenges, however, several of which are highlighted in this research effort. These include engaging the community and coordinating amongst multiple decision makers, defining a clear community objective, and planning with long decision horizons. Using the urban area of Portsmouth, Virginia as a case study, this work in progress paper demonstrates a decision approach which addresses these critical elements of community-driven disaster operations management.

Abstract: Crowd sensing data (also known as crowdsourcing) are of great significance to support flood risk management. With the growing volume of available data in the past few years, researchers have used in situ sensor data to filter and prioritize volunteers' information. Nevertheless, stationary, in situ sensors are only capable of monitoring a limited region, and this could hamper proper decision-making. This study investigates the use of weather radar precipitation to support the processing of crowd sensing data with the goal of improving situation awareness in a disaster and early warnings (e.g., floods). Results from a case study carried out in the city of São Paulo, Brazil, demonstrate that weather radar data are able to validate flooded areas identified from clusters of crowd sensing data. In this manner, crowd sensing and weather radar data together can not only help engage citizens, but also generate high-quality data at finer spatial and temporal resolutions to improve the decision-making related to weather-related disaster events.

Abstract: One of the most critical challenges faced by authorities during the management of a climate-related crisis is the overwhelming flow of heterogeneous information coming from humans and deployed sensors (e.g. cameras, temperature measurements, etc.), which has to be processed in order to filter meaningful items and provide crisis decision support. Towards addressing this challenge, ontologies can provide a semantically unified representation of the domain, along with superior capabilities in querying and information retrieval. Nevertheless, the recently proposed ontologies only cover subsets of the relevant concepts. This paper proposes a more “all-around” lightweight ontology for climate crisis management, which greatly facilitates decision support and merges several pertinent aspects: representation of a crisis, climate parameters that may cause climate crises, sensor analysis, crisis incidents and related impacts, first responder unit allocations. The ontology could constitute the backbone of the decision support systems for crisis management.

Abstract: Nowadays large amounts of climatology data, including daily precipitation data, are collected by means of sensors located in different locations of the world. The data driven analysis of these large data sets by means of scalable machine learning and data mining techniques allows extracting interesting knowledge from data, inferring interesting patterns and correlations among sets of spatio-temporal events and characterizing them. In this paper, we describe the PERCEIVE framework. PERCEIVE is a data-driven framework based on frequent spatio-temporal sequences and aims at extracting frequent correlations among spatio-temporal precipitation events. It is implemented by using R and Apache Spark, for scalability reasons, and provides also a visualization module that can be used to intuitively show the extracted patterns. A preliminary set of experiments show the efficiency and the effectiveness of PERCEIVE.

Abstract: One of the most important infrastructures for society is the payment system. If this system malfunctions, it can lead to disruptions in other critical infrastructures. By developing resilience i.e. the ability to recover or resist different disruptions in complex dynamic systems, as well as analyzing and raising awareness about it, such events can hopefully be handled better. The purpose of this study is to gain an insight into the different sectors' ways of dealing with disturbances in the payment system. Six participants from the food, fuel and bank sectors were interviewed using semi-structured questions. The interviews were conducted and data was analyzed using a thematic analysis approach. The results indicate a low level of resilience maturity among these organizations when it comes to handle long-term disruptions in the payment systems. The results provide valuable input to the project and a better understanding of payment infrastructure resilience.

Abstract: The profusion of information technology has created new possibilities for local communities to self-organize and respond to disruptive events. Along with the opportunities, there is also a series of challenges that need to be addressed in order to improve societal resilience. One of these challenges is to make sense of the continuous stream of information to create a coherent understanding and improve coordination. The research presented in this paper focuses on the socio-technical requirements of IT platforms that support sense-making and coordination. Using a comprehensive evaluation exercise based on real data from the 2017 Kenyan elections, we examine the development, workflows and use of this shared situational awareness in a group decision making process. In this manner, we identify requirements for resilience platforms and identify further research directions.

Abstract: Emerging threats provide motivation to develop new methods for preparing the next generation of crisis responders. Bayesian theory shifts reasoning toward a probabilistic, epistemic paradigm, giving rise to Evans' revised heuristic-analytic theory. Researchers at The Pennsylvania State University use scenario-based training and the analytic decision game (ADG) to blend and implement these processes as foundational pedagogy for engaging, educating and training medical students as crisis responders and critical thinkers. The ADG scenarios vary by content and level of expertise, lending themselves readily adaptable to both crisis response preparation and the development of clinical reasoning. The ADG creates a virtual crisis requiring participants to engage in scenario management as role-players. For the past two years, medical students from the Penn State College of Medicine, in their first year of training, have participated in the ADG Lights Out scenario, testing community preparation and resilience after a wide-spread and months-long power outage.