Forecasting landslides using SIGMA model: a case study from Idukki, India

Abstract

This study proposes a regional landslide early warning system for Idukki (India), using a decisional algorithm. The algorithm forecasts the possibility of occurrence of landslide by comparing the rainfall thresholds with the cumulated rainfall values. The region has suffered severe socio-economic setbacks during the disastrous landslides that happened in 2018 and 2019. Rainfall thresholds are defined for Idukki, using the total amount of precipitation cumulated at different time intervals ranging from 1 to 30 days. The first three-day cumulative values were used for evaluating the effect of short-term rainfall and the remaining days for the effect of long-term rainfall. The derived thresholds were calibrated using historical landslides and rainfall data from 2009 to 2017, optimized to reduce the false alarms and then validated using the 2018 data. The validation results show that the model is effectively predicting 79% of the landslides that happened in the region during 2018 and can be easily integrated with a rainfall forecasting system for the prediction of landslides. The model can be further improved with the availability of better spatial and temporal resolution of rainfall data and can be used as an effective tool for predicting the occurrence of landslides.