Forecasting Transfer Accuracy in Urban Resilience Projects with Cross-Domain Modeling

Abstract

The increasing frequency and severity of environmental and anthropogenic disruptions have positioned urban resilience as a critical imperative for modern city planning. While predictive modeling has emerged as a cornerstone for developing robust urban resilience strategies, the application of models developed in one urban context to another frequently results in significant degradation of predictive performance. This paper investigates the predictability of transfer accuracy in cross domain modeling through a structured case comparison methodology applied to urban resilience projects. By systematically analyzing the divergence between source and target urban domains, this study seeks to establish a comprehensive framework for anticipating how well resilience models will perform when transplanted across different municipal and geographical settings. The research utilizes extensive comparative analysis of infrastructural, socioeconomic, and environmental variables to isolate the factors most responsible for model degradation during transfer. The findings demonstrate that cross domain transfer accuracy can be reliably predicted by quantifying the multidimensional distance between case studies before actual model deployment. This preemptive predictive capability allows urban planners and policymakers to gauge the viability of adopting external resilience frameworks, thereby optimizing resource allocation and mitigating the risks associated with model failure in critical infrastructure planning. Ultimately, this study contributes to the broader general academic discourse by providing an empirically grounded methodology for evaluating model portability in complex, multi-agent urban systems.