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.
Highlights
Landslides are frequent natural disasters that have severe effects on lives and properties in hilly terrains (Muhammad et al 2010; Abd Majid and Rainis 2019)
The conventional rainfall thresholds consider the short-term effect of rainfall, or the parameters associated with the immediately preceding rainfall event for identifying the critical conditions. Such thresholds are used for predicting the occurrence of future landslides (Althuwaynee and Pradhan 2017) and can be used as a part of regional Landslide Early Warning System (LEWS) (Ahmed et al 2020)
This study is an attempt to develop a regional scale LEWS to reduce the risk due to landslides in the region, using Sistema Integrato Gestione Monitoraggion Allerta (SIGMA) model, which has more than 20 years of operational experience
Summary
Landslides are frequent natural disasters that have severe effects on lives and properties in hilly terrains (Muhammad et al 2010; Abd Majid and Rainis 2019). The conventional rainfall thresholds consider the short-term effect of rainfall, or the parameters associated with the immediately preceding rainfall event for identifying the critical conditions Such thresholds are used for predicting the occurrence of future landslides (Althuwaynee and Pradhan 2017) and can be used as a part of regional Landslide Early Warning System (LEWS) (Ahmed et al 2020). Some attempts have been made for forecasting landslides in parts of Western Ghats using rainfall thresholds (Abraham et al 2019, 2020b; Thennavan et al 2020) and antecedent soil wetness (Abraham et al 2021) These models are not ready to be used in an operational LEWS due to the higher number of false alarms or the complexities associated with the model. This study is an attempt to develop a regional scale LEWS to reduce the risk due to landslides in the region, using SIGMA model, which has more than 20 years of operational experience
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
