Information Technology in Disaster Risk Reduction

Systemic risks are embedded in the complex networks of an increasingly interconnected world. Achieving the Sendai Framework for Disaster Risk Reduction, the Paris Agreement on Climate Change 2015 and the 2030 Agenda for Sustainable Development require that risk mitigation involves not only experts but ‘power-brokers’ – those with the power to act. Impactful risk assessment and mitigation development requires high levels of ownership of the assessment and mitigation strategies, and so needs to be done fast and involve relatively small amounts of the power-brokers time. This requirement means that the analysis of the risk system will need to be transparent and relevant. We describe a method employing causal mapping with experts and power-brokers stakeholders. These stakeholders interactively undertake a qualitative systemic risk assessment and subsequently develop and agree strategies for risk mitigation explicitly considering (i) the direct purpose of mitigation (the other risks that are likely to be at least partly mitigated – the risks that are directly linked from the mitigated risk), and also (ii) the negative goals that will be mitigated.

Training and measurement of situation awareness (SA) in a dynamic decision-making context is a complex task and depends on personal factors as well as time, space, and situation. Therefore, to measure first-level incident commanders’ SA several authentic and immersive virtual simulations of rescue incidents should be used. In this paper, we report on Effective Command and the dynamic decision-making assessment methodology as a theoretical framework for SA assessment and implementation of virtual reality software using sixteen different virtual simulations in an organization. First, we report four aspects needed to be implemented for virtual simulation-based assessment of SA. Next, we analysed the differences between three SA ascending levels: perception, comprehension, and prediction that might be influenced by the scenario storyline. According to prior research, we would expect the difficulty of the three SA levels to be in ascending order and we evaluated that by analysing N = 665 assessment results. We confirmed such an ascending order for most scenarios, and we also identified these scenarios for which this assumption was not met. Finally, we discuss the possibilities of analysing the scenario-based SA level differences in the future when assessing SA with more automated measures. We argue that by improving the assessment of SA, we can foster the training possibilities for incident commanders’ dynamic decision-making.

Disaster management is a crucial process that aims at limiting the consequences of a natural disaster. Disaster-related data, that are heterogeneous and multi-source, should be shared among different actors involved in the management process to enhance the interoperability. In addition, they can be used for inferring new information that helps in decision making. The evacuation process of flood victims during a flood disaster is critical and should be simple, rapid and efficient to ensure the victims’ safety. In this paper, we present an ontology that allows integrating and sharing flood-related data to various involved actors and updating these data in real time throughout the flood. Furthermore, we propose using the ontology to infer new information representing evacuation priorities of places impacted by the flood using semantic reasoning to assist in the disaster management process. The evaluation results show that it is efficient for enhancing information interoperability as well as for inferring evacuation priorities.

Floods are one of the major natural disasters that contribute to the high disaster death rate in the global south, especially in Nigeria. This requires an effort of collaboration from all stakeholders in designing, building, deploying, and maintaining inclusive disaster management systems. This effort can hardly be achieved without the application of universal design principles in designing rescue applications. In this research, we have studied situational disabilities and their effect on situational awareness based on the data collected from victims in Nigeria through a questionnaire with 56 respondents and 5 follow-up interviews. The research work contributed to identifying situational disabilities and their corresponding accessibility barriers based on difficulty experiences collected from the research participants in Nigeria.

Crises and incidents are threatening conditions that require urgent action. In emergencies, the lack of relevant information can affect and increase the risks of the crisis scenarios. In this context, response time is crucial for responding to the needs of affected individuals and environments. This study presents an analysis of six awareness criteria applied in emergency response and how these criteria could improve the technological and collaborative solution supported by drones, called “Drones to the Rescue”. This research aims to understand how awareness criteria are considered in real emergency response and how incorporating those into the solution would improve it. To evaluate the potential improvement, we carried out a survey with experts from the Firefight Department, Civil Defense, Military Police, and Armed Forces from the states of Rio de Janeiro and Minas Gerais – Brazil. This research concludes that awareness criteria bring new possibilities and provide new alternatives in emergency response situations.

Ensuring the telecommunication services is one of the key pillars for the disaster response. However, the past studies pointed out that the lack of electricity causes significant negative effect in the service continuity of telecommunication in emergencies. Using the vehicle to establish a mobile and off-grid telecommunication site is one of the solutions in emergencies. Recently, the vehicle with the external power supply functions becomes popular as the progress of the electric vehicles in our society. Therefore, this study aims to assess the practical effectiveness of the application of the fuel cell vehicle to support the telecommunication services in emergencies. The medical fuel cell vehicle with telecommunication functions and the external power supply functions was developed. In the field test, various functions in the M-FCV such as the external power supply, the real-time image sharing, the vehicle management application were tested to support the telecommunication in emergencies. In addition, in a disaster response drill, the M-FCV was utilized to establish the field telecommunication unit by using its functions. To ensure the electricity to support the telecommunication services in emergencies, the application of fuel cell vehicles is considered as an effective solution.

The tremendous challenges brought on by the COVID-19 pandemic has brought to light the significance of inclusive and effective risk communication as a top priority for preparedness and response in health emergencies. A comprehensive emergency management strategy must include effective communication systems assisted with state-of-the-art Information and Communication Technologies (ICTs). For enabling communication, SMS messages utilizing the vast telecommunication sector have proven to be a very efficient tool. Messages should be precise, scientifically accurate, context-sensitive, entrusted, understandable, as well as relevant for the segment of society they are intended for. Geo-targeting helps in reducing over-alerting by minimizing the annoyance and the subsequent opt-out behavior caused by unnecessary alerts. This study provides an in-depth description of the established platform known as ‘COVID-19 Sāvdhān’, which allows for the SMS dissemination of pandemic-related messages to the geo-targeted population, including but not limited to information about vaccination, quarantine facilities, testing centers, hotspots, lockdown, essential supplies, and law and order situations. The platform has been widely used to disseminate more than 3.4 billion targeted SMS in 10 different languages across the length and breadth of India during COVID situation.

Earthquake destroyed many buildings, especially wooden ones, in Japan. Collecting information regarding collapsed buildings during the emergency phase (i.e., 72 h after a disaster) is difficult but essential for rescue activities. This study developed an automatic model to detect collapsed buildings using multiple object tracking (MOT) from aerial videos. Roof damage and pancake collapse are destructions unique to traditional Japanese buildings. Previous studies that detected collapsed buildings using the features of debris or damage failed to discriminate between collapsed and held-up buildings when the buildings have the above Japanese feature. Therefore, this study used the deep learning MOT model to classify collapsed and held-up buildings regardless of debris appearance. The recall and precision of each track of collapsed buildings were 29.1% and 36.7%, respectively, based on cross-validation with the drone video of the 2016 Kumamoto Earthquake. Analysis between the recall and other factors indicated that the aspect ratio, speed, and appearance time of the buildings were significant features for the detection. In the relationship between recall and these factors, we deduce that the recall of track increases to 63.9% if the drone operator films aerial videos effectively. Moreover, this study analyzed effective drone filming and flying way to satisfy some conditions for detection. This result provides recommended filming guides to drone operators for future earthquakes.

The threat posed by chemical, biological, radiological, and nuclear (CBRN) agents is considered greater than ever before. Technological advances offer new opportunities for prevention and decontamination in the fight against terrorism, but also pose new challenges for affected areas, companies, and forces. This article is based on the project “CBRN - City Sensor Network”. The aim was to use stationary sensor networks to achieve early, automatic detection, identification, and protection against attacks with CBRN substances in public enclosed spaces with a focus on transport companies. In this paper, special attention is given to the challenges and factors that need to be considered when implementing sensor networks for emergency plans from a social science perspective. In doing so, processes and factors were defined to enable blue light organisations to limit damages faster, increase the safety of their own employees and improve the reconstruction of accidents. With a mix of methods, social science developed a phase model that depict the process of detecting and determining CBRN and extracted the challenges of implementation and deployment into a theme map.

The paper presents needs and requirements for information systems support for collaborative emergency management, developed in collaboration with emergency management stakeholders in Norway. The requirements focus on three basic elements for shared situation awareness (SA) in inter-agency emergency response: terminology harmonization, map-based common operational picture (COP), and support for evaluation and learning from incidents. Building on core design principles for emergency management information systems, prototypes have been developed for these three areas and validated with potential users. The paper contributes with a user-centric approach in identifying and designing information systems support for collaborative emergency management together with stakeholders, moving from needs to requirements to design proposals and covering core elements of COPs needed for shared SA. The collected requirements and prototypes developed may serve as a basis for further development of standardized solutions for inter-agency emergency operations.

As the COVID-19 pandemic is a multi-agency, long-lasting crisis with a complex information structure, the netcentric approach in the crisis operations would be expected to show its advantages. However, the implementation of netcentric operations seems to be met with challenges during the COVID-19 pandemic. This research investigates the capability of organizations to adapt the netcentric approach, specifically in relation to information sharing in changing contexts. Thereby, the factors that influence inter-organizational information sharing within netcentric operations are examined. It can be concluded that in practice, re-applying the principles of netcentric operations to a different context can be challenging. More specifically, over time, the netcentric operations become ingrained in process, systems, and tools. While this codified and institutionalized netcentric approach supports the daily information exchange in emergencies, it also reduces the ability of organizations to adapt their approaches to new requirements dictated by changing circumstances.

The Risk and Resilience Assessment Center (RiskAC) of East Macedonia and Thrace Region (REMTH) is a research project aiming at supporting decision-making on the repression and prevention of disasters in the geographical region of REMTH in Greece. The project's objective is to establish a new, properly equipped, and organized research center up to the Technological Readiness Level 5 (TRL5) by August 2023, dedicated to risk and resilience assessment studies of possible hazardous scenarios within REMTH. This paper will present the gaps identified between work as imagined vs. work as done by the RiskAC center, which is currently under development, during a plane crash emergency in REMTH in July 2022. Although RiskAC analyses related to the plane crash emergency validated that the project achieved the goal of creating a center that reached the TRL 5 maturity level, it identified points for further improving its processes. Lastly, the paper highlights the importance of considering safety via creating Concept of Operation Documents, among other things, as early as possible as essential aspects in effective operating such systems.

During the last years, International Organizations for Standardization have been developing many quality management tools which are being implemented by several types of organizations. These tools are especially being implemented to manage disasters and reduce activity disruptions’ consequences. However, monitoring the performance of the organizational norm requires developing metrics and assessment tools that can be used easily and on a daily basis to make organizations more resilient to crisis situations. The management tool that this research work considers is the “Business Continuity Plan” known as BCP. One way to assess this type of plan is to define, design then test quantifying and qualifying keys to measure its robustness.

As societies become more connected, the nature and response to crises are becoming increasingly complex as well. Crises can affect our societies in unpredictable ways and thus require different actors to bring information together to take appropriate actions. However, as the number and diversity of actors increases, so does the quality and variety of information systems. Especially considering the increased availability and accessibility of technologies available together, process and exchange information.

While these developments provide a high potential to improved information sharing, these options also present certain risks. Individual organizations may have measures, training, and policies in place for daily routines to mitigate these risks. During a crisis however, these measures can become a constraint, especially when sharing information in an inter-organizational and cross-boundary context. People in the crisis response team may not appreciate the risks, need to improvise, or even circumvent measures.

In this paper, we examine the increased cybersecurity risks associated with the increased use of information technologies used in and facilitating information management during a crisis response. Using a serious gaming research method we examine how, in the context of crisis response, these factors are exacerbated under the pressure of time, uncertainty and coordination challenges. From this we identify the need for increased awareness about the risks of information technologies and sharing in heterogenous stakeholder environments. Specifically, a cultural change and need for additional capacities to understand, assess, and mitigate risks involved as the number of actors, systems, and technological options keep increasing.

Protecting valuable IT assets is one of the most significant challenges that organizations face today. Cyber criminals operating beyond physical boundaries, are able to disrupt and destroy cyber infrastructure, deny organizations access to IT services, and steal sensitive data. In response, enterprises organize security operations centres at the heart of their entities with the purpose of employing socio-technical systems with capabilities to detect, analyze and respond to these threats. This exploratory study examines how such capabilities are operationalized in leading “Managed Security Service Providers” (MSSPs) providing cybersecurity operations and incident response, and looks at how situation awareness knowledge is constructed through the organizational levels of the enterprise detection and response. In this context, situational awareness span over different levels in the organization starting from team personnel, ending at top management. Our work contributes to situational awareness theory in the context of cybersecurity operations and incident response. Thus, we advance the understanding of the organizational capabilities of MSSPs to develop awareness of the cyber-threat landscape and the broader operational dynamics. By introducing InCReASE, a dynamic framework towards enhancing situation awareness in Security Operations Centers (SOC) operations and incident response; we extend existing situational awareness models, combining elements of the existing body of knowledge and our empirical findings. The presented work is a reflection on the best practices adopted by MSSPs organizations operating in Norway.

This paper provides the results of the privacy impact assessment conducted for the PROACTIVE solutions and its subsequent privacy by design integration process. PROACTIVE is a solution for fostering coordination and communication between stakeholders before and during CBRNe events focusing on providing guidance to vulnerable populations. Based on extensive fieldwork activities with practitioners and vulnerable groups, the results of the analysis reveal the main risk focus, including the need to filter data by competent authorities (data controllers) and potential solutions for each of them, which allow establishing standard procedures for systems used in similar contexts.

The main issue, which has existed since the development of Single Layer Hierarchical Graph Neuron (SLHGN) started, is the representation for location data that will be fed to SLHGN structure. Similar issue, the double-value characteristics of the ordinary and current coordinate system have slowed down the enhancement of a sophisticated earthquake forecasting technology that uses SLHGN. To deal with the problem, a new way of representing locations on the earth, called Single Value Coordinate System (SVCS), has been researched and developed. Since the location of a potential earthquake—after being elaborated by the earthquake forecaster—is represented through esvecees (SVCS) values, people would have difficulties to understand and to locate it. To make the earthquake forecasting results be understandable and locatable for targeted people, those esvecees values should therefore be transformed into ordinary coordinates, which comprise longitude and latitude values. For that purpose, a technology for the transformation from esvecees values to ordinary coordinates have been successfully developed. The experiment results show that the location of a potential earthquake can now be gained as longitude and latitude values. This means that the earthquake forecasting using Single Layer Hierarchical Graph Neuron (SLHGN) is getting closer to its complete functionalities.

Climate change occurs in the atmosphere over a long period due to the influence of the sun, oceans, clouds, ice, land, and living organisms on each other. This research used the Principal Component Analysis (PCA) model compounded with Vector Autoregressive Integrated (VARI) called the PCA-VARI model to determine climate change. PCA reduces correlated climate data to uncorrelated data expressed as main components containing a linear combination of initial variables. In the time series model, a non-stationary multivariate comprises more than two variables that influence each other, using differencing processes. A variety of two models was used simultaneously to forecasting future climate data. Analysis of climate parameters uses ten measurements variable located in five areas, namely Lembang, Bogor, Tasikmalaya, Sukabumi, and Indramayu, for twenty years, using POWER NASA Agro-climatology datasets. The methodology follows the Knowledge Discovery in Databases (KDD) in data mining for integrated PCA with VARI and post-processing using visualization by Impulse Response Function (IRF). The result of forecasting in the PCA-VARI model using IRF in the next six months showed that the effect of location climate on the response of other regions with changes in standard deviation is similar to adjacent locations. Meanwhile, the responses obtained varied based on the observation time for the five areas that are not close.

In order to minimize the damage inflicted by large-scale disasters, it is essential to collect and disseminate information quickly and accurately. In recent years, various national agencies and local municipalities have used Twitter and other highly immediate social media to help focus their disaster relief efforts. Because the volume of information circulating on social media increases rapidly during a disaster, the ability to quickly sort out valuable posts from the massive volume of posts that appear is essential. In the case of Twitter, it is vital for early responders to identify the location of relevant tweets in order to facilitate decision making and focus their response. To help in this task, attempts have been made to use machine learning to classify genres, extract useful information, and identify locations and points of interest for groups of tweets posted during a disaster. However, since preparing training data and building a model during the early stages of a disaster are extremely challenging, using a model built on past disaster tweet data offers a promising possibility. In this study, we focus on three heavy rain disasters that occurred in Japan and examine the extraction of the location mentions in tweets using models learned from tweets posted during prior disasters.

In the early stages of an emergency, information extracted from social media can support crisis response with evidence-based content. In order to capture this evidence, the events of interest must be first promptly detected. An automated detection system is able to activate other tasks, such as preemptive data processing for extracting event-related information. In this paper, we extend the human-in-the-loop approach in our previous work, TriggerCit, with a machine-learning-based event detection system trained on word count time series and coupled with an automated lexicon building algorithm. We design this framework in a language-agnostic fashion. In this way, the system can be deployed to any language without substantial effort. We evaluate the capacity of the proposed work against authoritative flood data for Nepal recorded over two years.

As a part of disaster preparedness, smartphone applications for disaster information sharing is considered as effective. There is a wide range of disaster prevention applications. Among them, we focus on obtaining knowledge of lives in an evacuation shelter, which are difficult to learn in daily lives. In this paper, we develop and evaluate a simulation application for living in an evacuation shelter using the concept of gamification (serious games) to gain knowledge and experience of living in an evacuation shelter. During playing the application, the users answer quizzes related to lives in a shelter, and are expected to learn knowledge on the lives. Two subject experiments are conducted to evaluate the effectiveness of the application by comparing with the results of learning evacuation shelter information by only by documents. Currently, the application is freely available as a Mac or Windows application.

Although the Covid-19 pandemic triggered a significant public health challenge to humanity, measures like lockdowns let to cascading effects that impacted the food system to such extent, that its rebuilding became an imminent issue. As part of the food system, “of alternative food networks (AFNs) and emergency food networks (EFNs) did not escape to that change. In fact, because of their predominant community-based background, they highlighted their local capabilities as an asset for the food system to be adaptive from supply chain disruptions during the crisis. Concerned by the exacerbation of food insecurity, which because of the magnitude of crisis could not be exclusively handled by public-private established organizations, these stakeholders supported on social solidarity to keep service levels as stable as possible, not only by struggling to keep their already existing supply and distribution channels running, but also creating new ones. Through a case study approach, this paper characterises and analyses eight community-based initiatives that either rebuilt or started up a food supply chain during the Covid-19 outbreak peaks in Medellín, Colombia. In addition, based on a prospective planning approach, this research identified change factors that had the potential to become a long-term capability to install resiliency in the food system when future threats set on. Finally, a strategic approach is developed as an input to impact local food security risk policies through disaster risk reduction under the frame of preparedness, as well as for the food system resiliency in non-emergency contexts.

This paper provides a comprehensive design of an inventory to gather and manage resilience-related information. A conceptual architecture is introduced followed by an Agile approach by breaking the development task into four sprints, which involves constant collaboration with community users, offering continuous improvement, and getting approval from stakeholders at every stage. By facilitating an agile implementation process, we ensure that the inventory development is highly dynamic, flexible, and collaborative.