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The SESAMO early warning system for rainfall-triggered landslides

  • Autori: Pumo, D.; Francipane, A.; Lo Conti, F.; Arnone, E.; Bitonto, P.; Viola, F.; La Loggia, G.; Noto, L.
  • Anno di pubblicazione: 2016
  • Tipologia: Articolo in rivista (Articolo in rivista)
  • Parole Chiave: Artificial neural network; Early warning; Integrated information system; MEMS tilt sensor; Meteorological micro radar; Monitoring system; Atmospheric Science; Geotechnical Engineering and Engineering Geology
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The development of Web-based information systems coupled with advanced monitoring systems could prove to be extremely useful in landslide risk management and mitigation. A new frontier in the field of rainfall-triggered landslides (RTLs) lies in the real-time modelling of the relationship between rainfall and slope stability; this requires an intensive monitoring of some key parameters that could be achieved through the use of modern and often low-cost technologies. This work describes an integrated information system for early warning of RTLs that has been deployed and tested, in a prototypal form, for an Italian pilot site. The core of the proposed system is a wireless sensor network collecting meteorological, hydrological and geotechnical data. Data provided by different sensors and transmitted to a Web-based platform are used by an opportunely designed artificial neural network performing a stability analysis in near real-time or in forecast modality. The system is able to predict whether and when landslides could occur, providing early warnings of potential slope failures. System infrastructure, designed on three interacting levels, encompasses a sensing level, integrating different Web-based sensors, a processing level, using Web standard interoperability services and specifically implemented algorithms, and, finally, a warning level, providing warning information through Web technologies.