Advancing landslide early warning systems through saturation monitoring and predictive analysis

Abstract

The landslide occurrence is most common in the rainy season, costing deep damage to the infrastructure and human lives. An early prediction framework for such a disaster can help to mitigate damages. For this reason, in this work, a prediction framework for shallow landslide initiation is developed and validated with a real case study. In order to test the reliability of the prediction framework, a back analysis of a recent landslide accrued in the study area, Malin village of Maharashtra, on July 2014 was performed. Relations of landslide stability with the water saturation were established through a physically based approach using the Geo-Studio analysis module. A leaky barrel algorithm was developed for the study locations to monitor rainfall's effect on water saturation. Simulation results of landslide stability were compared with the leaky barrel-based rainfall-water saturation algorithm. The result confirms the good predictability of landslide occurrence through a presented framework. The procedure presented in this paper for predicting shallow landslide occurrence is recommended for real-time monitoring of landslide-prone locations.