Lam, N.S.N.; Wang, K., and Mihunov, V., 2024. The Resilience Inference Measurement (RIM) approach to measuring and predicting community resilience to coastal hazards. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 33-37. Charlotte (North Carolina), ISSN 0749-0208.

Improving community resilience to coastal hazards has been a key societal issue and studied widely by multiple disciplines. However, despite the huge literature on community resilience to coastal hazards, there is no consensus on the best approach to measuring it. We first provide an overview of the challenges in measuring community resilience to natural hazards. We then describe our effort in developing the Resilience Inference Measurement (RIM) model, which is designed to overcome two major challenges in resilience measurement: (i) the lack of empirical validation to support the derived indices and their associated indicators influencing the resilience level, and (ii) the lack of statistical inferential power. We highlight how RIM can be applied to different types of hazards and its extension to dynamic resilience analysis via methods such as Bayesian Networks. We conclude that through more experiments with the RIM approach, it is possible to develop a set of common resilience predictors that would address multiple hazards simultaneously.