Abstract
Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted.
Highlights
Rainfall triggered landslides are one of the most devastating naturally occurring disasters across the world [1]
[51,52,53]. to Inunderstand the case of identify statisticalpotentially approaches, it is assumed sections that the parameters affecting studies around the world have been conducted towards the development of landslide susceptibility events in the past will be the same in future [54], and these analyses can be categorized into bivariate maps (LSM) using various,the there seemsaffecting to be no consensus to the best method and multivariate
Validation of the susceptibility maps was performed for randomly selected data from the inventory data using receiver operating characteristics (ROC)
Summary
Rainfall triggered landslides are one of the most devastating naturally occurring disasters across the world [1]. The two main techniques used to assess temporal probability for future landslide occurrences are (i) analysis of potential slope failure and (ii) statistical analysis of past landslide events [13,15]. An indirect approach analyzing the frequency of occurrence of rainfall was used in this study to determine temporal probability. As the frequency of rainfall-induced landslides only evaluates how often landslides might occur, it needs to be integrated with spatial probability (susceptibility) and temporal probability to develop a landslide hazard map [17,18]. These studies have primarily focused on rainfall estimation and spatial assessment, using various techniques such as a probabilistic approach [5,32], a semi-automatic algorithm approach [26], an empirical approach [4], and machine learning models [33].
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