AI for Anticipatory Action: Moving Beyond Climate Forecasting

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.

References in corpus (6)

• Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
• A Unifying Causal Framework for Analyzing Dataset Shift-stable Learning Algorithms
• Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen
• Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport
• Predictive modeling of movements of refugees and internally displaced people: Towards a computational framework
• NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters