AI for Anticipatory Action: Moving Beyond Climate Forecasting
It aims to improve disaster response for vulnerable populations by identifying gaps in machine learning applications, though it appears incremental as it reviews and highlights areas rather than presenting new methods.
The paper addresses the shift from climate forecasting to anticipatory action in disaster response, focusing on how machine learning can help assess climate impacts on vulnerable populations to enable proactive measures, but does not report specific results or numbers.
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.