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<div> <table><tbody><tr><td> <p>The concept of disaster risk is multidisciplinary by nature. Responding to disasters and increasingly preventing new and reducing existing disaster risk has become the backbone of various disciplines. Yet, moving from various disciplinary perspectives to integrated approaches remains a fundamental challenge. This talk reflects on the experience of a group of early-career researchers, including physical scientists, engineers and social scientists from different organisations and countries, who came together to lead the refinement, operationalisation and testing of a risk-informed decision support environment (DSE) for Tomorrow’s Cities. Drawing on the notion of “boundary objects” and reflexive elicitation, members of the group explored enabling and hindering factors to interdisciplinary research across four case studies that unfolded between July-December 2021, namely: operationalisation process of the DSE; development of a testbed as a demonstration case for the implementation of the DSE; consolidation of frequently asked questions about the DSE; and elaboration of a multi-media communication tool for outreach to various audiences. The study argues that enablers of interdisciplinarity can be synthesised across a range of factors, including exogenous, governing, learning and attitudinal, and that diversity of boundary objects as convening spaces for disciplinary interaction can propel integration. It is further suggested that a similar rationale can be applied when moving towards co-producing knowledge with non-academic actors in a transdisciplinary manner. Strengthening the interdisciplinary capacities of early career researchers across disciplines and geographies is a fundamental step and promising pathway towards transformation.</p> </td> </tr></tbody></table></div>

Abstract. For an effective disaster risk mitigation plan and for building a society more resilient to natural disasters, it is essential to understand the factors that are related to social vulnerability as an important dimension to social risk. This study aims to identify the associations between socio-economic and socio-demographic household characteristics and earthquake related social vulnerability using survey data collected from 41,093 households in Istanbul. Machine learning models, namely: logistic regression, classification tree, random forest, support vector machine, naive bayes, artificial neural network, and K-nearest neighbours, were employed to classify households according to their social vulnerability status. Due to the disparity of class size for the outcome variable, subsampling strategies were applied for dealing with imbalanced data. Artificial Neural Network (ANN) was found to have the optimal predictive performance when random majority under sampling was applied (AUC: 0.813). The results from the ANN method indicated that not having social security, living in a squatter house and having high risk of job loss after an earthquake were among the most important predictors for increasing social vulnerability risk. Additionally, the level of education, the ratio of elderly persons in the household, owning a property, household size, ratio of income earners, and having savings were associated with vulnerability. An open access R-shiny web application was developed to visually display the performance of ML methods, important variables for the social vulnerability risk classification and the spatial distribution of the variables across Istanbul neighbourhoods. The machine learning methodology and the findings that we present in this paper can serve as a guidance for decision makers in identifying and prioritising action towards target groups to reduce their vulnerability risk prior to earthquakes.