# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 3 Results
### The Importance of Sensors in Early Warning Systems  
Sensors play a crucial role in EWSs. They collect real-time data, which are extracted into databases and can be integrated into various systems, including the Geographic Information System (GIS) [48]. They are fundamental for data collection to assess crisis events, their severity, or for developing predictive models, as well as for analysing past events and their extent. In addition to real-time data, empirical data are also valuable for evaluating the impact or severity of crisis events. Their role in EWSs within crisis management is particularly significant. Depending on their configuration, sensors detect the presence of input information and its value.  
Ideally, information is automatically evaluated using machine learning or artificial intelligence, and warnings are sent to at-risk populations within seconds. This is especially crucial for earthquakes, where machine-based evaluation is essential for applying ground motion prediction equations (GMPEs) [49]. These equations enable the calculation of the anticipated affected area of an earthquake. One of the most advanced earthquake EWSs is in Japan, where sensors detect P-waves across a wide network. The system then distributes alerts to the affected population a few seconds before the arrival of stronger seismic waves. The system can also estimate the magnitude and intensity of the earthquake [50].  
Sensors are also widely used in flood EWSs. There are several ways to use them effectively, but one of the most critical parameters for flood risk and EWSs is water level height. During rainfall events, river levels rise due to natural runoff from the terrain. The most common cause of flooding is a river overflowing its banks due to intense or prolonged rainfall [51].  
Figure 1: Model of an EWS based on the three-component approach.  
When assessing flood risk, two key indicators are essential for predictive models and evaluating flood severity:  
* Water level height;
* Flow velocity.  
Flow velocity or water discharge helps estimate the anticipated water level height in a given section of the river. Using empirical data, it is possible to calculate both discharge and inflow from real-time data. If discharge is lower than inflow, water accumulates, leading to a rise in water levels. The water level height directly reflects the destructive nature of floods and their potential formation [52; 53].  
However, for early warning purposes, real-time data are the most critical. It is essential to integrate meteorological stations and sensor networks into crisis management structures and ensure their continuous evaluation [54]. By analysing a digital elevation model (DEM), it is possible to identify locations where the riverbed is shallowest and narrowest. When combined with empirical data and rating curves, these models help identify areas where river overflow is most likely. Water level sensors should be installed at these critical locations [55].  
An alert should be sent to at-risk populations when:  
* The river level at a sensor site reaches a critical threshold;
* The rate of water level increase suggests that overflow will occur in the near future.  
Another key parameter is flow velocity, which provides insights into river discharge. If inflow exceeds discharge at certain points, water accumulates, increasing the risk of localised flooding.  
These examples illustrate how sensors can be used to monitor crisis event formation. However, their implementation is possible for many other crisis events beyond earthquakes and floods. Selecting appropriate sensors is essential, both for monitoring the factors that trigger crisis events and for detecting the actual manifestation of those events.