Modeling the neighborhood boosts landslide prediction

2020-12-13 - 10:42

KAUST CEMSE AMCS EXTSTAT STAT Landslide Prediction — In developing the landslide model, KAUST researchers used data from 3379 weather-triggered landslide events that occurred on 889 slopes in the Collazzone region, Italy.

© Valerio Mei / Alamy Stock Photo

An effective hazard warning system aims to predict the time, place, size, and frequency of landslides, yet there are multiple complex and often random input factors to consider. Researchers have developed a computer model that improves on existing prediction accuracy and enhances understanding of the complexities inherent in landslide events.

“Existing landslide models work from a premise where each slope in an area is assigned a value of zero or one — the slope is either stable or unstable,” says former KAUST postdoc, Luigi Lombardo, now at the University of Twente in the Netherlands. This project builds on previous landslide models developed by Lombardo, continuing his collaboration with Raphaël Huser and the team from KAUST.

KAUST CEMSE AMCS EXTSTAT STAT Improving Prediction Methods for Landslides — Improving prediction methods for landslides could give communities and disaster management agencies better warning systems, which could save lives. The red arrows show potential future landslide points; the red shaded area is a current hazard zone. © 2020 KAUST

“Assigning a binary value means that critical details about a slope and its neighborhood are lost,” continues Lombardo. “For our model, we assigned values according to how many landslide events a given slope has experienced over time. In the case of our test region in the Collazzone area in Italy, this includes landslide data from detailed local records dating back around 100 years.”

Read the full article