The ‘What If’ framework: A scenario-based counterfactual approach to modelling urban resilience and disaster impact

Abstract

Urban growth in hazard-prone regions is frequently shaped by informal settlement expansion. Yet, disaster impact assessments typically evaluate only observed post-event conditions and do not consider how urban development trajectories might have evolved in the absence of disruptive events. This study applies a counterfactual modelling approach (‘What-If’ framework) that integrates deep learning–based multi-temporal land-cover mapping with Cellular Automata (CA) simulation to analyse alternative urban development pathways. The framework is implemented in Mocoa, Colombia, following the 2017 debris-flow disaster. Multi-temporal land-cover maps were generated using a U-Net deep learning architec..