Engineers Australia

Real-time monitoring and wireless data transmission to predict rain-induced landslides in critical slopes

Tharindu Abeykoon, Chaminda Gallage, Biyanvilage Dareeju and Jessica Trofimovs


Real-time landslide monitoring is an effective technique to minimise landslide risks, especially in circumstances where the potential for structural countermeasures is limited. Rainfall infiltration is considered as one of the most significant factors triggering slope instability. Hence real-time monitoring of parameters: rainfall, volumetric water content and surface deformations/displacements in the soil, enable the early detection of landslides, thus reducing the adverse impacts of landslides. This study involves low cost and simple to install miniature ground inclinometers equipped with MEMS (Micro Electro Mechanical Systems) tilt sensors, volumetric water content sensors, temperature sensors, a rain gauge and a wireless data transmission unit (DTU) for the prior identification of possible slope failure. The DTU receives data from sensor units via radio signal transmission at a higher data acquisition frequency and automatically transmits them via the mobile network to an internet server, and updates in an online web interface for the determination of slope instability. The monitoring programme in operation for more than two years in the Lake Baroon Catchment, Maleny plateau, Australia, accurately captured both creep movement of the slope with wetting and drying cycles and mass movements triggered by rainfall. The current study analysed the surface deformation and rainfall data produced by the real-time monitoring system and validated results using published study outcomes. Herein, a combination of rainfall data, I-D threshold equations and ground tilting rate was identified as a more suitable measure to detect possible slope failure in advance. Further, the issue of a ‘precaution’ at a tilting rate 0.01˚/hr, and a ‘warning’ at 0.1˚/hr is recommended by this study along with the consideration of rainfall data.