The structure of LEWS can be schematized as an interrelation of the following main components: monitoring, modelling, forecasting, warning, response. However, tools, instruments, methods employed can vary considerably with the scale of analysis, as well as the characteristics and the aim of the warnings/alerts issued. For instance, at local scale instrumental devices are mostly used to monitor deformations and hydrogeological variables with the aim of setting thresholds for evacuation or interruption of services. At regional scale hydro-meteorological thresholds are widely used to prepare a timely response of civil protection and first responders. Concerning modelling techniques, analyses on local slopes generally allow for the use of numerical models, while statistical, probabilistic and physical-based models are widely used for large areas.
This session focuses on LEWS at all scales and stages of maturity, from prototype to active and dismissed ones. Test cases describing operational application of consolidated approaches are welcome, as well as works dealing with promising recent innovations, even if still at an experimental stage.
Contributions addressing the following topics will be considered positively:
- real-time monitoring systems (IoT)
- prediction tools for warning purposes
- in-situ monitoring instruments and/or remote sensing devices
- warning models for issuing warning
- operational applications and performance analyses
- machine learning techniques applied for early warning purposes
Orals: Thu, 1 May | Room 1.15/16
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 3
EGU25-851 | ECS | Posters virtual | VPS12
Using volumetric water content measurements with the implementation of machine learning for monitoring shallow landslides induced by rainfallMon, 28 Apr, 14:00–15:45 (CEST) | vP3.25