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

Michele Ronco (), José María Tárraga, Jordi Muñoz, María Piles, Eva Sevillano Marco, Qiang Wang, Maria Teresa Miranda Espinosa, Sylvain Ponserre and Gustau Camps-Valls
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Michele Ronco: Universitat de València
José María Tárraga: Universitat de València
Jordi Muñoz: Universitat de València
María Piles: Universitat de València
Eva Sevillano Marco: Universitat de València
Qiang Wang: Universitat de València
Maria Teresa Miranda Espinosa: Internal Displacement Monitoring Centre (IDMC)
Sylvain Ponserre: Internal Displacement Monitoring Centre (IDMC)
Gustau Camps-Valls: Universitat de València

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-43809-8

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