Leveraging Water Data in a Machine Learning–Based Model for Forecasting Violent Conflict

The research introduces a random forest model capable of capturing 86 percent of future conflicts, defined as organized violence causing at least 10 fatalities over 12 months. While water-related variables correlate with conflict outcomes, they are not empirically significant for the model's decision-making unless the definition of conflict is adjusted, such as by lowering the fatality threshold. A web-based tool has been developed to allow users to explore these forecasts and indicators to support water-related peace-building interventions.

• The random forest model used to forecast conflict—defined as organized violence resulting in at least 10 fatalities over a 12-month period—captures 86 percent of future conflicts when applied to test data, although half of these predictions are false positives.
• Water-related variables correlate with conflict outcomes but are not empirically significant for the model's decision-making process, though their significance increases if the fatality threshold for conflict is lowered or if the model focuses only on emerging conflict.

APA

World Resources Institute (n.d.). Leveraging Water Data in a Machine Learning–Based Model for Forecasting Violent Conflict. https://www.wri.org/research/leveraging-water-data-machine-learning-based-model-forecasting-violent-conflict

Chicago

World Resources Institute. Leveraging Water Data in a Machine Learning–Based Model for Forecasting Violent Conflict. n.d. https://www.wri.org/research/leveraging-water-data-machine-learning-based-model-forecasting-violent-conflict.

Wikipedia

{{cite report |author=World Resources Institute |title=Leveraging Water Data in a Machine Learning–Based Model for Forecasting Violent Conflict |url=https://www.wri.org/research/leveraging-water-data-machine-learning-based-model-forecasting-violent-conflict |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldresourcesinstitutendleveraging, author = {{World Resources Institute}}, title = {{Leveraging Water Data in a Machine Learning–Based Model for Forecasting Violent Conflict}}, institution = {World Resources Institute}, url = {https://www.wri.org/research/leveraging-water-data-machine-learning-based-model-forecasting-violent-conflict}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }