AI for Anticipatory Action: Moving Beyond Climate Forecasting

TL;DR

This paper discusses how machine learning can enhance anticipatory disaster response by focusing on specific population impacts of climate change, moving beyond traditional climate forecasting methods.

Contribution

It provides an overview of anticipatory action, reviews machine learning applications, and identifies challenges and opportunities for improving disaster response for vulnerable populations.

Findings

Machine learning models are powerful for climate forecasting but need adaptation for anticipatory action.

Methodological gaps hinder the use of machine learning in proactive disaster response.

Identifies key areas where machine learning can improve response for vulnerable populations.

Abstract

Disaster response agencies have been shifting from a paradigm of climate forecasting towards one of anticipatory action: assessing not just what the climate will be, but how it will impact specific populations, thereby enabling proactive response and resource allocation. Machine learning models are becoming exceptionally powerful at climate forecasting, but methodological gaps remain in terms of facilitating anticipatory action. Here we provide an overview of anticipatory action, review relevant applications of machine learning, identify common challenges, and highlight areas where machine learning can uniquely contribute to advancing disaster response for populations most vulnerable to climate change.