Predictive monitoring of urban slope instabilities using geophysics and wireless sensor networks

Abstract

Landslides are a frequent natural hazard that affect millions of people globally and cause considerable damage and fatalities each year. Changing climate patterns and expanding urban areas are leading to an increased landslide risk. Thus, there is a need for novel methods to mitigate the hazard. Here, we provide an overview of recent work conducted within the densely populated San Francisco Bay Area, where geophysical characterization and monitoring are used to gain a predictive understanding of landslide processes. First, we show how geophysical and remote sensing can be used to map the landslide hazard, and then we show how geophysical data can be used to estimate the temporal variability of the hazard and possibly to provide landslide early warning. To estimate variations in soil properties and deformations across the site, we installed a wireless sensor network. We show how data from this network can be used to provide a predictive estimation of critical conditions. Eventually, the data presented here will be used by site management to address and mitigate the landslide hazard.