Abstract: Evaluation and testing are major steps in the development of any information system, particularly if it is to be used in high-risk contexts such as conflicts. While thus far there are various approaches for testing against technology requirements; usability or usefulness, there is a lack of a comprehensive evaluation framework that combines the three elements. The lack of such a framework and commonly agreed standards constitutes a barrier for innovation, and at the same time imposes risks to responders if the technology is introduced without proper testing. This paper aims to close this gap. Based on a reviewing of evaluation methods and measurement metrics, we design a comprehensive evaluation framework including common code quality testing metrics, usability testing methods, subjective usefulness questionnaires, and performance indicators. We demonstrate our approach by using the example of an integrated system for the safety and security of humanitarian missions, and we highlight how our approach allows measuring the system?s quality and usefulness.

Abstract: The increasing use of social media as an information source brings further challenges – social media platforms can be an excellent medium for disseminating public awareness and critical information, that can be shared across large populations. However, misinformation in social media can have immense implications on public health, risking the effectiveness of health interventions as well as lives. This has been particularly true in the case of COVID-19 pandemic, with a range of misinformation, conspiracy theories and propaganda being spread across social channels. In our study, through a questionnaire survey, we set out to understand how members of the public interact with different sources when looking for information on COVID-19. We explored how participants react when they encounter information they believe to be misinformation. Through a set of three behaviour tasks, synthetic misinformation posts were provided to the participants who chose how they would react to them. In this work in progress study, we present initial findings and insights into our analysis of the data collected. We highlight what are the most common reactions to misinformation and also how these reactions are different based on the type of misinformation.

Abstract: Microblogs are an opportunity for scavenging critical information such as sentiments. This information can be used to detect rapidly the sentiment of the crowd towards crises or disasters. It can be used as an effective tool to inform humanitarian efforts, and improve the ways in which informative messages are crafted for the crowd regarding an event. Unique characteristics of microblogs (lack of context, use of jargon etc) in Tweets expressed by a message-sharing social network during a disaster response require special handling to identify sentiment. We present a systematic evaluation of approaches to accurately and precisely identify sentiment in these Tweets. This paper describes sentiment detection expressed in 3698 Tweets, collected during the September 2010, San Bruno, California gas explosion and resulting fires. The data collected was manually coded to benchmark our techniques. We start by using a library of words with annotated sentiment, SentiWordNet 3.0, to detect the basic sentiment of each Tweet. We complemented that technique by adding a comprehensive list of emoticons, a sentiment based dictionary and a list of out-of-vocabulary words that are popular in brief, online text communications such as lol, wow, etc. Our technique performed 27% better than Bayesian Networks alone, and the combination of Bayesian networks with annotated lists provided marginal improvements in sentiment detection than various combinations of lists. © 2012 ISCRAM.

Abstract: The proliferation of unmanned aerial systems (i.e., drones) can provide great value to the future of search and rescue. However, with the increase adoption of such systems, issues around hybrid human-drone team coordination and planning will arise. To address these early challenges, we provide insights into the development of testbeds in the form of mixed reality games with simulated drones. This research presents an architecture to address challenges and opportunities in using drones for search and rescue. On this architecture, we develop a mixed reality game in which human players engage with the physical world and with gameplay that is purely virtual. We expect the architecture to be useful to a range of researchers an practitioners, forming the basis for investigating and training within this unique, new domain.

Abstract: Given the importance of geographic information for riverine flood evacuations, a geographic information system (GIS) is a vital tool for supporting successful flood evacuation operations. This paper discusses the development of a GIS-based riverine flood evacuation model which used to model trip distributions between flooded areas and relocation shelters. As the ultimate goal of this research is to simulate, model, and optimise a planned evacuation, all components of evacuation time have been considered (e.g., travel time between flooded areas and relocation shelters, warning time for each flooded area, and the time needed for evacuation before these areas get inundated). As well, variation in population (static and dynamic population) within the flooded areas has been considered.

Abstract: Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS' functionality can be used to determine landslides related to the recent Typhoon Haiyan.

Abstract: Disasters can severely disrupt the flow of Fast-Moving Consumer Goods (FMCGs) in New Zealand (NZ), preventing the replenishment of essential products and causing shortages on retailers’ shelves. This paper presents work-in-process research that aims to better understand how the NZ FMCG retailers build resilience into their replenishment operations to mitigate disruptions in the wake of a disaster. The two key components of supply chain resilience (redundancy and flexibility) are investigated in the context of the 2021 Canterbury flooding. A survey was used to collect data on retailers’ routine replenishment operations, the impacts of the flooding, and practices mitigating disruptions. The preliminary findings suggest that redundant inventory is used to compensate for insufficient flexibility in the NZ freight system (due to not only the lack of adequate secondary roads and alternative modes of transport, but also the centralised distribution system limiting the sources of supply). This study contributes a better understanding of the FMCG distribution and replenishment operations in NZ and highlights the need for public and private investments (e.g. redundant transport infrastructure and distribution facilities). Additional research investigating the most influential investments to improve the ability of the FMCG sector to manage post-disaster freight disruptions would benefit the literature.

Abstract: Innovative technologies such as monitoring the quality of surface water aquifers with satellite images, applying UAV (Unmanned Aerial Vehicle) and drone technology for a variety of operations, water quality measurements with improved techniques along with IoT (Internet of Things) and ICT (Information and Communication Technologies), can provide sufficient data for enhancing water safety in urban water utilities. Specifically, these data could be an effective tool for improving risk assessment process and management of water supply systems. Nevertheless, till now, there is a relative lack of published works that validate the efficiency of combing these technologies on water safety processes by incorporating most of them with a systematic way and during real working conditions in water utilities. This work aims to present the preliminary design concept of a platform that embraces innovating water safety technologies planned to be applied to Thessaloniki's Water Supply and Sewerage Co. S.A Standard Operating Procedures (SOP).

Abstract: This position paper discusses the ongoing debate on the topic of humanitarian effectiveness and highlights the need to look at the structuring of the humanitarian system from an ethical point of view. The paper makes the argument that any community engagement with the affected population is a matter of ethics and dignity of those needs to be considered as a central focus to the discussion. The paper also discusses how the humanitarian system as a set of technology, tools & practises focusses on efficiency, and overshadows the issues of care by objectifying and turning the affected population into mere numbers. We discuss that technology and organisations are inter-twined and such a discussion of technology cannot be contemplated without considering the traditional institutional practises as a whole.

Abstract: We propose an OWL/SWRL enabled ontological environment which can play a role in reporting and decision making in Humanitarian Crises (HC). We use (5WH): WHO, WHAT, WHERE, WHEN, WHY and HOW, as the main vehicle for gathering information for decision making. We implement the semantics of (5WH) through OWL models and perform reasoning with SWRL rules, in order to support decision making and create more efficient Humanitarian Response (HR). Our case study shows the feasibility of the proposal and its outcome. © 2012 ISCRAM.

Abstract: Micro-blogging site like Twitter, over the last decade, has evolved into a proactive communication channel during mass convergence and emergency events, especially in crisis stricken scenarios. Extracting multiple levels of information associated with the overwhelming amount of social media data generated during such situations remains a great challenge to disaster-affected communities and professional emergency responders. These valuable data, segregated into different informative categories, can be leveraged by the government agencies, humanitarian communities as well as citizens to bring about faster response in areas of necessity. In this paper, we address the above scenario by developing a deep Convolutional Neural Network (CNN) for multi-label classification of crisis related tweets.We augment deep CNN by several linguistic features extracted from Tweet, and investigate their usage in classification. Evaluation on a benchmark dataset show that our proposed approach attains the state-of-the-art performance.

Abstract: Efficient request and deployment of critical resources for urban search and rescue operations is vital to emergency response. This paper presents a RFID (Radio Frequency Identification) supported system for on-site data collection to communicate structural condition, to track search and rescue status, and to request and allocate appropriate resources. The system provides a unified interface for efficient posing, gathering, storing and sharing of building assessment information. Visualization and easy access of such information enables rescuers to response to the disaster with better situational awareness. Resource requests are sent to the GIS resource repository service that enables a visual disaster management environment for resource allocation. Request and deployment of critical resources through this system enables lifesaving efforts, with the appropriate equipment, operator, and materials, become more efficient and effective. System development at the Illinois Fire Service Institute has shown promising results.