Toward an integrated disaster management approach: How artificial intelligence can boost disaster management

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters

Predictive analytics in humanitarian action: a preliminary mapping and analysis

This rapid review research provides the most comprehensive mapping and analysis of predictive analytic initiatives in humanitarian aid to date. It documents 49 projects including a variety of novel applications (see Appendix for details). It provides a typology of predictive analytics in digital humanitarianism and answers a series of key questions about patterns of current use, ethical risks and future directions in the application of predictive analytics by humanitarian actors. The study took 14 days in May 2020. Forty-nine predictive analytics projects were mapped and analysed according to the main phases of the humanitarian cycle, type of predictions made, sector of application, geography of application, and technical approach used. Despite the limitations of rapid response research, some preliminary recommendations are made on the basis of the findings including: i) Governments, humanitarian agencies, funders and private companies should publish more open data in order to further extend the potential for predictive analytics; ii) Humanitarian agencies should apply the precautionary principle in data collection, data safeguarding and responsible data to protect vulnerable populations from harm; iii) To align practice with humanitarian principles and commitments, predictive analytics actors need to include affected populations in all aspects of the design and project cycle; iv) Funding of predictive analysis should be tied to risk assessment, risk mitigation and knowledge sharing on the ethics and downside-risks of predictive analytics; v) Funders should support the emerging ecosystem to develop geographical or thematic specialisms, convene knowledge-sharing events and produce ethical guidelines for practice; vi) Further research is necessary to build on this preliminary mapping and analysis in this crucial and rapidly developing area of humanitarian action vii) Primary research interviews with humanitarian agencies and key informants would make it possible to validate claims and establish the current status and future plans of initiatives; viii) A small number of case studies would improve depth of understanding about approaches being used and proposed pathways to scale; ix) Focus groups or a workshop would surface agency experience of risks and barriers not shared in publicly accessible documents and enable lesson learning

Disaster management in smart cities

The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.info:eu-repo/semantics/publishedVersio

Human Resource Management in Emergency Situations

The dissertation examines the issues related to the human resource management in emergency situations and introduces the measures helping to solve these issues. The prime aim is to analyse complexly a human resource management, built environment resilience management life cycle and its stages for the purpose of creating an effective Human Resource Management in Emergency Situations Model and Intelligent System. This would help in accelerating resilience in every stage, managing personal stress and reducing disaster-related losses. The dissertation consists of an Introduction, three Chapters, the Conclusions, References, List of Author’s Publications and nine Appendices. The introduction discusses the research problem and the research relevance, outlines the research object, states the research aim and objectives, overviews the research methodology and the original contribution of the research, presents the practical value of the research results, and lists the defended propositions. The introduction concludes with an overview of the author’s publications and conference presentations on the topic of this dissertation. Chapter 1 introduces best practice in the field of disaster and resilience management in the built environment. It also analyses disaster and resilience management life cycle ant its stages, reviews different intelligent decision support systems, and investigates researches on application of physiological parameters and their dependence on stress. The chapter ends with conclusions and the explicit objectives of the dissertation. Chapter 2 of the dissertation introduces the conceptual model of human resource management in emergency situations. To implement multiple criteria analysis of the research object the methods of multiple criteria analysis and mahematics are proposed. They should be integrated with intelligent technologies. In Chapter 3 the model developed by the author and the methods of multiple criteria analysis are adopted by developing the Intelligent Decision Support System for a Human Resource Management in Emergency Situations consisting of four subsystems: Physiological Advisory Subsystem to Analyse a User’s Post-Disaster Stress Management; Text Analytics Subsystem; Recommender Thermometer for Measuring the Preparedness for Resilience and Subsystem of Integrated Virtual and Intelligent Technologies. The main statements of the thesis were published in eleven scientific articles: two in journals listed in the Thomson Reuters ISI Web of Science, one in a peer-reviewed scientific journal, four in peer-reviewed conference proceedings referenced in the Thomson Reuters ISI database, and three in peer-reviewed conference proceedings in Lithuania. Five presentations were given on the topic of the dissertation at conferences in Lithuania and other countries

Emergency Services Workforce 2030: Changing landscape literature review

The Changing Landscape Literature Review collates a high-level evidence base around seven major themes in the changing landscape (i.e., the external environment) that fire, emergency service, and rural land management agencies operate in, and which will shape workforce planning and capability requirements over the next decade. It is an output of the Workforce 2030 project and is one of two literature reviews that summarise the research base underpinning a high-level integrative report of emerging workforce challenges and opportunities, Emergency Services Workforce 2030. Workforce 2030 aimed to highlight major trends and developments likely to impact the future workforces of emergency service organisations, and their potential implications. The starting point for the project was a question: What can research from outside the sphere of emergency management add to our knowledge of wider trends and developments likely to shape the future emergency services workforce, and their implications? The seven themes included in the Changing Landscape Literature Review are: 1) demographic changes, 2) changing nature of work, 3) changes in volunteering, 4) physical technology, 5) digital technology, 6) shifting expectations, and changing risk. A second, accompanying literature review, the Changing Work Literature Review, focuses on another nine themes related to emergency service organisation’s internal workforce management approaches and working environments

Application of artificial neural networks and colored petri nets on earthquake resilient water distribution systems

Water distribution systems are important lifelines and a critical and complex infrastructure of a country. The performance of this system during unexpected rare events is important as it is one of the lifelines that people directly depend on and other factors indirectly impact the economy of a nation. In this thesis a couple of methods that can be used to predict damage and simulate the restoration process of a water distribution system are presented. Contributing to the effort of applying computational tools to infrastructure systems, Artificial Neural Network (ANN) is used to predict the rate of damage in the pipe network during seismic events. Prediction done in this thesis is based on earthquake intensity, peak ground velocity, and pipe size and material type. Further, restoration process of water distribution network in a seismic event is modeled and restoration curves are simulated using colored Petri nets. This dynamic simulation will aid decision makers to adopt the best strategies during disaster management. Prediction of damages, modeling and simulation in conjunction with other disaster reduction methodologies and strategies is expected to be helpful to be more resilient and better prepared for disasters --Abstract, page iv