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AI for Anticipatory Action: Moving Beyond Climate Forecasting

Benjamin Q. Huynh, Mathew V. Kiang

TL;DR

This paper addresses the shift from climate forecasting to anticipatory action by surveying how machine learning can enhance impact-based forecasting and population-specific risk prediction. It formalizes problem formulations for correlational and causal forecasting, and discusses exposure-response functions as a bridge from climate events to human impacts, highlighting methods to estimate these links via ML and satellite-derived damage data. The authors identify critical gaps—exposure-response estimation, dataset shift, data scarcity, and fairness—and discuss approaches such as transportability and invariant prediction to improve robustness and causal validity. They also emphasize ethical considerations, data inequities, and practical constraints in deploying anticipatory action, arguing for AI-enabled improvements that can lead to better resource allocation and protection for vulnerable populations. Overall, the work provides a roadmap for integrating ML into anticipatory action to reduce disaster losses and save lives.

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.

AI for Anticipatory Action: Moving Beyond Climate Forecasting

TL;DR

This paper addresses the shift from climate forecasting to anticipatory action by surveying how machine learning can enhance impact-based forecasting and population-specific risk prediction. It formalizes problem formulations for correlational and causal forecasting, and discusses exposure-response functions as a bridge from climate events to human impacts, highlighting methods to estimate these links via ML and satellite-derived damage data. The authors identify critical gaps—exposure-response estimation, dataset shift, data scarcity, and fairness—and discuss approaches such as transportability and invariant prediction to improve robustness and causal validity. They also emphasize ethical considerations, data inequities, and practical constraints in deploying anticipatory action, arguing for AI-enabled improvements that can lead to better resource allocation and protection for vulnerable populations. Overall, the work provides a roadmap for integrating ML into anticipatory action to reduce disaster losses and save lives.

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.
Paper Structure (8 sections, 7 equations, 2 figures, 1 table)

This paper contains 8 sections, 7 equations, 2 figures, 1 table.

Table of Contents

  1. Background
  2. Challenges and Opportunities
  3. Problem Formulation
  4. Open Areas for Improvement
  5. Exposure-response functions
  6. Dataset Shift
  7. Data Scarcity and Algorithmic Bias
  8. Discussion

Figures (2)

  • Figure 1: Typical timeline of disaster preparedness, anticipatory action, and response. Disaster risk reduction refers to strengthening resilience before crises occur, and anticipatory action refers to proactive response and resource allocation after an early warning system flags an incoming disaster.
  • Figure 2: Typical workflow for the forecasting portion of anticipatory action. After population-specific impact forecasts are provided, targeted resource allocation can be conducted.