Integrated Early Warning System Based on Community Monitoring and Artificial Intelligence: Methodological Framework for the Mulato River Sub-basin (Mocoa, Colombia)

This work presents the methodological framework of the HIDROANDES project, involving the participatory installation of rainfall and streamflow monitoring stations in indigenous and rural communities of the Mulato River sub-basin (Mocoa, Colombia). Precipitation and water level measurements constitute the foundation for the development of an integrated early warning system aimed at reducing vulnerability to rapid-onset flooding events.

The proposed methodology consists of three interconnected components. First, real-time community-based monitoring, in which local actors operate hydrometeorological stations, generating geo-referenced datasets while integrating traditional knowledge and ensuring inclusive participation. Second, AI-assisted hydrological modelling, based on neural networks trained with locally generated and synthetic data to capture the specific hydrological response dynamics of the basin. Third, a generation of tailored alerts, designed according to the socio-territorial characteristics of each community and supported by fast-response predictive models capable of issuing warnings within seconds.

The central hypothesis of this research states that AI-driven, locally tailored hydrological models trained with community-generated data will provide faster and more accurate flood predictions than conventional hydrological models, especially in steep, fast-responding Andean basins such as the Mulato River.

This methodological approach is expected to strengthen local capacities for risk management, improve anticipatory response to extreme events, and provide a replicable framework for early warning systems in vulnerable Andean–Amazonian watersheds.

Keywords: community-based monitoring, early warning systems, artificial intelligence, participatory hydrology, rapid-response basins, flood risk management.