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Systematic Mapping of Global Research on Disaster Damage Estimation for Buildings: A Machine Learning-Aided Study
(MDPI (Basel, Switzerland), 2024-06-20) Rajapaksha D; Siriwardana C; Ruparathna R; Maqsood T; Setunge S; Rajapakse L; De Silva S; Witt E; Bilau AA; Sun B
Research on disaster damage estimation for buildings has gained extensive attention due to the increased number of disastrous events, facilitating risk assessment, the effective integration of disaster resilience measures, and policy development. A systematic mapping study has been conducted, focusing on disaster damage estimation studies to identify trends, relationships, and gaps in this large and exponentially growing subject area. A novel approach using machine learning algorithms to screen, categorise, and map the articles was adopted to mitigate the constraints of manual handling. Out of 8608 articles from major scientific databases, the most relevant 2186 were used in the analysis. These articles were classified based on the hazard, geographical location, damage function properties, and building properties. Key observations reveal an emerging trend in publications, with most studies concentrated in developed and severely disaster-affected countries in America, Europe, and Asia. A significant portion (68%) of the relevant articles focus on earthquakes. However, as the key research opportunities, a notable research gap exists in studies focusing on the African and South American continents despite the significant damage caused by disasters there. Additionally, studies on floods, hurricanes, and tsunamis are minimal compared to those on earthquakes. Further trends and relationships in current studies were analysed to convey insights from the literature, identifying research gaps in terms of hazards, geographical locations, and other relevant parameters. These insights aim to effectively guide future research in disaster damage estimation for buildings.
Differences in perceived sources of uncertainty in natural hazards science advice: lessons for cross-disciplinary communication
(Frontiers Media S.A., 2024-04-04) Doyle EEH; Thompson J; Hill SR; Williams M; Paton D; Harrison SE; Bostrom A; Becker JS; Tagliacozzo S
Introduction: We conducted mental model interviews in Aotearoa NZ to understand perspectives of uncertainty associated with natural hazards science. Such science contains many layers of interacting uncertainties, and varied understandings about what these are and where they come from creates communication challenges, impacting the trust in, and use of, science. To improve effective communication, it is thus crucial to understand the many diverse perspectives of scientific uncertainty. Methods: Participants included hazard scientists (n = 11, e.g., geophysical, social, and other sciences), professionals with some scientific training (n = 10, e.g., planners, policy analysts, emergency managers), and lay public participants with no advanced training in science (n = 10, e.g., journalism, history, administration, art, or other domains). We present a comparative analysis of the mental model maps produced by participants, considering individuals’ levels of training and expertise in, and experience of, science. Results: A qualitative comparison identified increasing map organization with science literacy, suggesting greater science training in, experience with, or expertise in, science results in a more organized and structured mental model of uncertainty. There were also language differences, with lay public participants focused more on perceptions of control and safety, while scientists focused on formal models of risk and likelihood. Discussion: These findings are presented to enhance hazard, risk, and science communication. It is important to also identify ways to understand the tacit knowledge individuals already hold which may influence their interpretation of a message. The interview methodology we present here could also be adapted to understand different perspectives in participatory and co-development research.
Social Vulnerability Indicators for Flooding in Aotearoa New Zealand
(MDPI (Basel, Switzerland), 2021-04-09) Mason K; Lindberg K; Haenfling C; Schori A; Marsters H; Read D; Borman B
Social vulnerability indicators are a valuable tool for understanding which population groups are more vulnerable to experiencing negative impacts from disasters, and where these groups live, to inform disaster risk management activities. While many approaches have been used to measure social vulnerability to natural hazards, there is no single method or universally agreed approach. This paper proposes a novel approach to developing social vulnerability indicators, using the example of flooding in Aotearoa New Zealand. A conceptual framework was developed to guide selection of the social vulnerability indicators, based on previous frameworks (including the MOVE framework), consideration of climate change, and a holistic view of health and wellbeing. Using this framework, ten dimensions relating to social vulnerability were identified: exposure; children; older adults; health and disability status; money to cope with crises/losses; social connectedness; knowledge, skills and awareness of natural hazards; safe, secure and healthy housing; food and water to cope with shortage; and decision making and participation. For each dimension, key indicators were identified and implemented, mostly using national Census population data. After development, the indicators were assessed by end users using a case study of Porirua City, New Zealand, then implemented for the whole of New Zealand. These indicators will provide useful data about social vulnerability to floods in New Zealand, and these methods could potentially be adapted for other jurisdictions and other natural hazards, including those relating to climate change.

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