What question did this study set out to answer?

This research aims to optimize anticipatory action for agricultural communities facing climate hazards by using data-driven approaches.

Planning ahead: Optimizing anticipatory action via mixed integer programming and open-source dashboards to mitigate climate risk

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Randomized trial demonstrates optimized aid allocation in cyclone-prone regions, highlighting the benefits of anticipatory action.

This research aims to optimize anticipatory action for agricultural communities facing climate hazards by using data-driven approaches.
Implemented a spatially explicit, time-phased mixed-integer linear program to allocate resources.
Developed an open-source pipeline using Python, Pyomo for optimization, and Streamlit for visualization.
Applied the model in Purba Medinipur, India, to assess its impact on aid distribution.
Redirected up to 80% of funds to the most vulnerable areas, improving aid effectiveness.
Increased high-risk coverage from 31% to 65% compared to a uniform distribution.
Demonstrated operational scalability, solving large instances in under 15 seconds.

Clercq et al. (2026) studied this question.

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