Landslides, floods, debris flows, and local instability phenomena are not “absolutely unpredictable” events: they are often preceded by measurable signals (intense rainfall, soil saturation, rapid water level rises, microclimate variations) that become critical when not detected in time. The goal today is to move from a reactive to a predictive and operational approach, where continuous data collection drives decisions: deploying teams on the ground, closing underpasses, diverting traffic, securing infrastructure, and alerting communities. IoT solutions make it possible to establish a widespread network of distributed
sensors, transmitting data to central platforms or local edge gateways. This approach enhances territorial resilience by turning risk into a monitored variable rather than a surprise.
The “Heart” of Reliability: Simple, Robust, Low-Cost Sensors (Capacitive + Air Chamber)
In natural hazard monitoring, the challenge is not just to measure, but to measure reliably under harsh conditions (mud, debris, frost, heat, vibrations, limited power, intermittent connectivity). In this context, “simple” sensors can be the most effective, as they reduce points of failure and maintenance needs. A practical example is the use of capacitive sensors to detect the presence or progression of water: they are inexpensive, low-power, and, if well designed, more resistant to contamination and corrosion than direct electrode solutions.
A particularly interesting concept, from a robustness perspective, is the use of an air chamber or collection volume that fills during extreme rain or flooding events. The logic is simple: instead of measuring water “in the chaos” of a flow (turbidity, debris, foam), a protected and repeatable measurement point is created, where water enters according to a known dynamic (by gravity, capillarity, through a small conduit or grate). A capacitive sensor can then detect the water level or presence in that volume, generating a stable and easily interpretable signal. This setup allows compact devices to be installed in underpasses, strategic drains, embankments, low-lying areas, ditches, or at-risk infrastructure, with costs compatible with a widespread sensor network.
From Measurement to Alert: Correlation with Local Weather and Predictive Models
A single sensor is useful, but the real step forward occurs when data are correlated. A well-designed IoT system integrates: local data (water presence/level at critical points, rapid changes), weather data (rain gauges, rainfall intensity, event duration, wind, temperature), and in some cases geotechnical data (inclinometers, extensometers, deformations). Correlation allows ordinary phenomena to be distinguished from “anomalous” signals: it is not enough to know that it is raining; it is necessary to understand how the territory is responding.
Edge computing can make a difference: a local gateway can calculate simple but powerful indicators (rate of water level rise, saturation time, dynamic thresholds linked to rainfall intensity), generating immediate alerts even if the cloud connection is unstable. The cloud, on the other hand, consolidates historical data, generates reports, and supports more advanced analyses (seasonal patterns, comparison between points, updated risk maps). The
operational result is a system that does not send “continuous alarms” but qualified signals: when X occurs and local weather conditions indicate Y, the probability of a critical event rises and predefined procedures are triggered.
Application Example: Floods and Local Instability with Timely Alerts and Targeted Interventions
In a scenario like Umbria, with secondary watercourses, at-risk slopes, and vulnerable infrastructure, an IoT sensor network can monitor key points: sections where water levels rise rapidly, areas prone to water stagnation, underpasses and urbanized zones susceptible to flooding, and slopes where soil saturation can anticipate movements. If, during a heavy rainfall event, sensors detect accelerated filling of collection chambers (sign of anomalous water) and local rain gauges confirm a critical threshold, the system sends alerts to authorities with priority and geolocation. This enables targeted evacuations and containment interventions (preventive closures, temporary barriers, monitoring of sensitive points) before the event escalates into an emergency.
The key point is that predictive management does not always require “expensive and complex” sensors: often, a network of simple, robust, low-cost devices, well-designed and integrated with local weather data, is sufficient. Capacitive sensors and air-chamber solutions, precisely because they are essential and repeatable, can become a strategic component for building widespread alert systems that are cost-effective and truly maintainable over time.
