Integrating IoT Monitoring Data and Machine Learning for Flash Flood Forecasting: A Case Study in Amalfi

Flash floods represent a major hydrogeological hazard in fast-responding coastal catchments of southern Italy, where intense and short-duration rainfall can lead to sudden increases in stream water levels. In this context, the Amalfitan coast area constitutes a particularly relevant case study, as it has been historically affected by extreme meteorological events, flash floods, and hydrogeological instability, resulting in significant impacts on urban areas and infrastructure. This study presents the development and evaluation of a Random Forest algorithm, aimed at predicting stream water levels and analyzing conditions likely to trigger flash floods.

The model relies exclusively on dynamic data continuously acquired through an IoT-based monitoring network deployed within the study basin, installed in the municipality of Amalfi. The network includes soil water content and soil suction sensors installed at shallow depths, allowing the characterization of hydrological conditions within the topmost soil layers. These measurements are complemented by a stream level sensor and rain gauges distributed across the basin. The integration of these variables enables the definition of relationships between weather forcing and hydrogeological response of the catchment.

The available dataset was split into training and testing subsets to evaluate model performance. The Random Forest model predicted stream water level dynamics and identified potential flash flood conditions, with accuracy assessed using established performance metrics. The integration of in-situ IoT monitoring data and Machine Learning provides a powerful approach for flash flood prediction, as continuous environmental measurements can be automatically analyzed to identify early-warning signals, capture complex interactions between rainfall and stream water level, and support real-time decision-making in highly dynamic catchments. The future integration of the model into an operational early warning system is considered as a potential advancement, with the aim of enhancing flood risk management and mitigation strategies in Amalfi and similar high-risk catchments.