Inform@Risk

Inform@Risk

AlpGeotech, working within the German/Colombian Inform@Risk research consortium, developed the low-cost geosensor network that gives residents of Bello Oriente, an informal hillside settlement in Medellín, Colombia. Advance warning of rainfall-triggered landslides.

Problem Statement

Bello Oriente, an informally urbanized neighborhood on the urban–rural fringe of Medellín, sits on steep, geologically unstable slopes that are highly susceptible to rainfall-induced landslides. Like many self-built settlements in Latin America, it grew without formal planning or hazard mapping, leaving residents with no monitoring infrastructure and effectively no advance warning before a slope failure.

Conventional landslide early-warning technology is typically too costly and too rigid to deploy at the scale and density required in informal settlements, so a fundamentally different, low-cost and participatory approach was needed one that could be installed, maintained and trusted by the community it protects.

Solution

As part of the four-year Inform@Risk research project (2018–2022), funded under Germany’s BMBF CLIENT II programme, AlpGeotech’s engineering team designed and built the project’s cost-effective IoT geosensor network: compact MEMS-based motion and tilt sensors distributed across the slope, transmitting continuous ground-movement data over the low-power, long-range LoRa protocol to a central gateway station.

Combined with rainfall monitoring and a participatory warning process co-designed with the Bello Oriente community, the system translates raw sensor readings into a warning residents can understand and act on. Working alongside German and Colombian universities, research institutes and local agencies, AlpGeotech’s instrumentation became the technical backbone of one of the first landslide early-warning systems purpose-built for an informal settlement.

The project concluded in December 2022 with a working prototype, which the Municipality of Medellín has since continued operating in test mode.