Early Warning Systems for Rainfall Induced Landslides

Early Warning Systems for Rainfall Induced Landslides

Organizers

Amin Askarinejad
TU Delft, Netherlands
A.Askarinejad@tudelft.nl

Faraz Tehrani
Deltares, Netherlands
Faraz.Tehrani@deltares.nl

Michele Calvello
University of Salerno, Italy
mcalvello@unisa.it

 

Session Description

Shallow and rapid landslides triggered by rainfall cause significant damage to infrastructure annually and affect tens of thousands of lives in many parts of the world, particularly in mountainous and developing regions. The extent and frequency of the occurrence of such devastating events are directly affected by the geomorphological, geotechnical, climatic and hydrological conditions. It is expected that the predicted rise in the number of extreme meteorological events, accompanied by the concentration of population and infrastructure in sloping areas, deforestation and improper agricultural practices, will result in an increased number of casualties and impacts on infrastructure associated with landslides. These factors enhance the motivation for design and implementation of reliable Early Warning Systems.

In this special session, the most important technical components of the early warning systems for rainfall-induced landslides are discussed. They include, but not limited to, monitoring techniques, the data transmission methods, data analysis, and definition of warning levels. A special attention will be paid to the systems suitable for regions with low economic capabilities, by discussing the novel low-cost-reliable sensors and use of publically available data such as satellite images, geomorphological and meteorological information. Scientists, stake-holders, engineers and practitioners are welcome to submit their contributions on the following topics:

1. Advanced Monitoring techniques such as use of Satellite data, InSAR methods, and development of low-cost sensors
2. Novel data transmission methods
3. Modern data analysis and prediction methods such as: advanced time series analysis, data assimilation, and general use of supervised and unsupervised machine learning algorithms
4. Definition of precursors and warning levels utilising hybrid data-driven and physics-based methods