Abstract: Earthquake Early Warning System (EEWS) plays a major role during an earthquake in alerting the public and authorities to take appropriate safety measures during an earthquake. Generally, EEWSs use three types of algorithms to generate alerts during an earthquake; namely: source-based, ground motion or wavefield-based and on-site-based approaches. However, source-based algorithms are commonly used in most of EEWSs worldwide. A source-based EEWS uses a particular time frame of the P-wave of an earthquake to estimate the source parameters such as magnitude and the location of that earthquake with the support of P-wave detection and earthquake magnitude and location estimation algorithms. As the initial step of a research project which aims to explore the best use of P-waves to generate earthquake alerts, this Work in Progress paper (WiPe) presents the initial partial findings from an ongoing literature review on exploring the algorithms used for P-wave detection and earthquake magnitude estimation.

Abstract: Australia is frequently hit by bushfires. In 2009, the “Black Saturday” fires killed 173 people and burnt hectares of bush. As a result, a research commission was created to investigate, and concluded that several aspects could be improved, in particular better understanding of the population actual behaviour, and better communication with them. We argue that agent-based modelling and simulation is a great tool to test possible communication strategies, in order to deduce valuable insight for emergency managers before new fires happen. In this paper, we extend an existing agent-based model of the population behaviour in bushfires. Concretely, we added a communication model based in social sciences, and user interactivity with the model. We present the results of first experiments with dierent communication strategies, providing valuable insight for better communication with the population during such events. This model is still preliminary and will eventually be turned into a serious game.

Abstract: Flooding poses a significant and growing risk for many urban areas. Stormwater systems are typically used to control flooding, but are traditionally passive (i.e. have no controllable components). However, if stormwater systems are retrofitted with valves and pumps, policies for controlling them in real-time could be implemented to enhance system performance over a wider range of conditions than originally designed for. In this paper, we propose an autonomous, reinforcement learning (RL) based, stormwater control system that aims to minimize flooding during storms. With this approach, an optimal control policy can be learned by letting an RL agent interact with the system in response to received reward signals. In comparison with a set of static control rules, RL shows superior performance on a wide range of artificial storm events. This demonstrates RL's ability to learn control actions based on observation and interaction, a key benefit for dynamic and ever-changing urban areas.

Abstract: Post-disaster information processing is relevant for the continuous improvement of operations and the reductionof risks. The current methodologies for post-disaster review suffer from several limitations, which reduce their use as a way of translating narrative in data for qualitative and quantitative analysis. Learning or effective knowledge sharing need a common formalism and method. Ontologies are the reference tool for structuring information in a “coded” data structure. Using the investigation of disaster management during the 2017 hurricane season in the French West Indies within the scope of the ANR “APRIL” project, this contribution introduces a methodology and a tool for providing a graphical representation of experiences for post-disaster review and lessons learning, based on a novel approach to case-based ontology development.

Abstract: The use of social media is gaining more and more in importance in ordinary life, but also in conflicts and emer-gencies. The social big data, generated by users, is partially also used as a source for situation assessment, e.g. to receive pictures or to assess the general mood. However, the information's believability is hard to control and can deceive. Rumors, fake news and social bots are phenomenons that challenge the easy consumption of social media. To address this, our paper explores the believability of content in social media. Based on foundations of infor-mation quality we conducted a literature study to derive a three-level model for assessing believability. It summa-rizes existing assessment approaches, assessment criteria and related measures. On this basis, we describe several steps towards the development of an assessment approach that works across different types of social media.

Abstract: Various terms and approaches currently exist on outlining the constituent components of crisis management as well as their interrelations, focusing mainly on the effective communication between the members of the crisis management unit. A gap emerges regarding a high-level and holistic approach on crisis management that will have the organization’s preparedness as its main pillar. In this work, crisis is organized into three macro-stages, i.e., pre-crisis, crisis, post-crisis. Preparedness is conceptualized as an overarching concept that frames an organization’s crisis management approach to reduce its vulnerability in a potential crisis. The study focuses on developing a high-level tool to enhance the preparedness of water utilities. The tool aims to serve as a holistic crisis management framework to support stakeholders in qualitatively assessing and improving their level of preparedness. The “Preparedness against hazardous events” tool was the result of this work, which was positively assessed through experts’ evaluation.

Abstract: It is generally agreed in crisis management that a comprehensive visualization of the situation is crucial for an appropriate situational awareness of the staff personnel in control rooms. Therefore an expert group of fire officers in the German State North Rhine Westphalia developed a system for this purpose known as the “tactical wall”. The core of the system is a situation map of the relevant area with so-called tactical signs, i. e. defined graphic symbols for hazards, response units and tactical measures. Moreover, the assignment of response units to tactical sectors or staging areas as well as other relevant information such as the management organization is displayed at defined places within the wall. While the system was purely manual in its original version, a new digital version was recently developed. The user interfaces of this system are web-based and can by intuitively operated after a minor training effort.

Abstract: Social Media monitoring has become a major issue in crisis and emergencies management. Indeed, social media may ease the sharing of information between citizens and Public Safety Organizations, but it also enables the rapid spreading of inaccurate information. As information is now provided and shared by anyone to anyone, information credibility is a major issue. We propose an approach to detect rumor in social media. This paper describes our work on semantic graph based information fusion, enhanced with uncertainty management capabilities. The uncertainty management capability enables managing the dierent level of credibility of actors of an emergency (dierent PSO oÿcers and citizens). Functions for information synthesis, conflicting information detection and information evaluation were developed and test during experimentation campaigns. The synthesis and conflicting information detection functionalities are very welcome by end-users. However, the uncertainty management is a combinatorial approach which remains a limitation for use with large amount of information.

Abstract: Project management has become a standard in business. Unfortunately, the projects as well as companies are increasingly subject to major disruptions. In this context, it is of prime importance to have the ability to manage the risks inherent to these projects to best achieve their objectives. The existing approaches of crisis management in the literature no longer seem to be adapted to this new normality. The future of research lies in a more systematic crisis assessment and a better conceptualization of the uncertainty associated with risks. It is necessary to rely on the collection of heterogeneous data in order to maximize the understanding of the project environment and to find a way that best describes and visualizes the influence of crises on the project management processes. This article uses the POD approach and applies it in the context of project management to address these issues.

Abstract: In this paper, we describe our approach to the localization in GNSS-denied and risky unknown environments offirst responders (FRs). The INGENIOUS project is an EU funded project which is developing a new integratedtoolkit to support the operations of FRs. The micro indoor-drones (MINs) developed within the INGENIOUSproject represent a component of the toolkit which will support the localization of FRs in search-and-rescue (SAR)operations. In this paper, the concept behind the MINs and the current achievements are illustrated.

Abstract: Between 2018 and 2019, the Incident Streams track (TREC-IS) has developed standard approaches for classifying the types and criticality of information shared in online social spaces during crises, but the introduction of SARS-CoV-2 has shifted the landscape of online crises substantially. While prior editions of TREC-IS have lacked data on large-scale public-health emergencies as these events are exceedingly rare, COVID-19 has introduced an over-abundance of potential data, and significant open questions remain about how existing approaches to crisis informatics and datasets built on other emergencies adapt to this new context. This paper describes how the 2020 edition of TREC-IS has addressed these dual issues by introducing a new COVID-19-specific task for evaluating generalization of existing COVID-19 annotation and system performance to this new context, applied to 11 regions across the globe. TREC-IS has also continued expanding its set of target crises, adding 29 new events and expanding the collection of event types to include explosions, fires, and general storms, making for a total of 9 event types in addition to the new COVID-19 events. Across these events, TREC-IS has made available 478,110 COVID-related messages and 282,444 crisis-related messages for participant systems to analyze, of which 14,835 COVID-related and 19,784 crisis-related messages have been manually annotated. Analyses of these new datasets and participant systems demonstrate first that both the distributions of information type and priority of information vary between general crises and COVID-19-related discussion. Secondly, despite these differences, results suggest leveraging general crisis data in the COVID-19 context improves performance over baselines. Using these results, we provide guidance on which information types appear most consistent between general crises and COVID-19.

Abstract: Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can contribute to the situational awareness of emergency responders, who have a need for them to be categorised according to information types (i.e. the type of aid services the messages are requesting). We introduce a transformer-based multi-task learning (MTL) technique for classifying information types and estimating the priority of these messages. We evaluate the effectiveness of our approach with a variety of metrics by submitting runs to the TREC Incident Streams (IS) track: a research initiative specifically designed for disaster tweet classification and prioritisation. The results demonstrate that our approach achieves competitive performance in most metrics as compared to other participating runs. Subsequently, we find that an ensemble approach combining disparate transformer encoders within our approach helps to improve the overall effectiveness to a significant extent, achieving state-of-the-art performance in almost every metric. We make the code publicly available so that our work can be reproduced and used as a baseline for the community for future work in this domain.