Exploring interactions between socioeconomic context and natural hazards on human population displacement

Abstract

Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016–2021 is presented. We account for environmental, societal, and economic factors to predict the number of displaced persons per event in the affected regions. Here we show that displacements can be primarily attributed to the combination of poor household conditions and intense precipitation, as revealed through the interpretation of the trained models using both Shapley values and causality-based methods. We hence provide empirical evidence that differential or uneven vulnerability exists and provide a means for its quantification, which could help advance evidence-based mitigation and adaptation planning efforts.

Ronco and colleagues analyze disaster-induced movements in the presence of floods, storms, and landslides during 2016–2021, providing empirical evidence that differential vulnerability exists and quantifying its extent. They achieve this by employing explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data.