Learning drivers of climate-induced human migrations with Gaussian processes

TL;DR

This paper uses Gaussian Processes to identify key climate and structural factors driving human displacements due to floods and storms from 2017-2019, providing insights into migration patterns influenced by climate change.

Contribution

It introduces a novel application of Gaussian Processes to model climate-induced migration, integrating diverse meteorological and structural data across 27 countries.

Findings

Structural factors significantly influence displacement magnitude.

Models accurately reproduce migration flows at multiple scales.

Climate variables alone are insufficient to explain migration patterns.

Abstract

In the current context of climate change, extreme heatwaves, droughts, and floods are not only impacting the biosphere and atmosphere but the anthroposphere too. Human populations are forcibly displaced, which are now referred to as climate-induced migrants. In this work, we investigate which climate and structural factors forced major human displacements in the presence of floods and storms during the years 2017-2019. We built, curated, and harmonized a database of meteorological and remote sensing indicators along with structural factors of 27 developing countries worldwide. We show how we can use Gaussian Processes to learn what variables can explain the impact of floods and storms in the context of forced displacements and to develop models that reproduce migration flows. Our results at regional, global, and disaster-specific scales show the importance of structural factors in the determination of the magnitude of displacements. The study may have both societal, political, and economical implications.