Fig. 4: Pathways to promote smarter and more equitable infrastructure resilience modeling.

a Intelligent algorithms can model complex failure patterns without overfitting or underfitting, working also in settings where sparse observations requires using diverse data sources and techniques, such as semi-supervised and transfer learning. b Combining observations from multiple data sources can overcome data scarcity and improve model prediction quality. c The smart resilience perspective focuses on a continuous improvement of models’ performance by using observations that emerge over time related to different components of infrastructure resilience. d Guiding data collection by leveraging methods such as active learning will facilitate optimal and efficient resilience model development. e Biases in data or models must be assessed at different levels, for example at census tracts or individual households, to uncover if these are fairly and equitably distributed. f Attention is steered towards addressing data or model errors in locales with populations with increased vulnerabilities, allowing models to self-guide error minimization to improve equity in resilience modeling. (Icons © Google Material Icons).