We also explore whether water-related indicators are useful predictors of conflict. Water-related variables are assessed to be correlated with conflict outcomes, but not empirically significant for model decision-making. However, adjusting the definition of conflict, such as by lowering the fatality threshold or examining only emerging conflict, increases the signifi- cance of water variables. A web-based tool that houses the model allows users to explore forecasts and indicators spatially and through time, providing additional informa- tion on underlying vulnerabilities as a first step toward enabling timely, effective water-related interventions to mitigate conflict and/or build peace.
Source: Kuzma, S., P. Kerins, E. Saccoccia, C. Whiteside, H. Roos, and C. Iceland. 2020. “Leveraging Water Data in a Machine Learning–Based Model for ForecastingViolent Conflict.” Technical Note. Washington, DC: World Resources Institute. Available online.