In Italy, hydrogeological emergencies are a long-standing structural challenge, linked to the country’s terrain, climate, and land-use patterns, including agriculture and urban development. Landslides, avalanches, floods, river overflows, and coastal erosion can severely impact the land, putting people and the environment at risk. According to the ISPRA report “Hydrogeological Instability in Italy: Hazard and Risk Indicators”, 94.5% of Italian municipalities face hydrogeological risk. For landslides alone, data from the Hydrogeological Planning Framework (PAI) highlight the number of residents exposed to potential hazards.
To tackle this risk, Italy has developed a comprehensive alert system involving municipalities, Regional Functional Centers (CFR), and Regional Environmental Protection Agencies (ARPA), which issue weather bulletins and warning codes. Data come from multiple sources: weather forecasts, hydrogeological monitoring networks, satellites, and environmental sensors. Leveraging these data through predictive analytics represents a major advancement for agencies managing hydrogeological emergencies, providing strategic tools to anticipate events and plan interventions.
By processing large volumes of heterogeneous data, agencies can predict hydrogeological events with high confidence. Predictive analytics supports key Civil Protection objectives: anticipating natural events, pre-allocating resources, optimizing rescue operations, and speeding up public notifications. By identifying recurring patterns, predictive models help anticipate event evolution and inform decision-making.
Early Warning is a key benefit. Complex phenomena like landslides, avalanches, or rapid flash floods can be forecast with enough lead time to enhance response effectiveness. Advanced analytics allow for better prediction of ground impacts, enabling more efficient and targeted interventions.
Implementing predictive analysis involves combining multiple data sources according to predefined algorithms, following four core steps:
Identify data sources and collect information. Each hydrogeological event requires specific data. For flash floods, sources include weather radar, high-frequency rain gauges, and hydrometric sensors in minor water networks. For landslides, key sources include inclinometers, satellite remote sensing, and weather radar.
Train the Machine Learning model. AI algorithms require data from at least three different sources, as hydrogeological events depend on multiple variables, including weather, terrain morphology, and river flow.
Integrate territorial morphology. The terrain’s characteristics form the structural basis of risk analysis. They determine vulnerability levels and potential impacts. Morphology is a constant risk factor, providing essential context for emergency preparedness.
Evaluate propagation models. Predicting the ground impact of hydrogeological events depends on how they spread. River floods develop over longer periods and are relatively easier to forecast in urban areas. Flash floods, however, propagate rapidly and require different modeling approaches, accounting for sudden channel overflows, submerged underpasses, and sewer flooding.
The 15 July 2020 cloudburst in Palermo was unprecedented, causing extensive damage. Traditional forecasting models struggled due to a lack of historical data. Predictive analytics, however, can identify unusual climate patterns, simulate likely scenarios—even exceptional ones—and support critical operational decisions. While it cannot provide absolute certainty, predictive analysis significantly improves risk assessment, enhancing rescue operations, safeguarding community assets, and accelerating the return to normal life.