RAINFALL THRESHOLD FOR LANDSLIDE INITIATION IN GORKHA DISTRICT, CENTRAL NEPAL
DOI:
https://doi.org/10.64862/Keywords:
Landslides, Rainfall threshold, CTRL-t algorithm, LEWS, Bootstrapping, OLS, PythonAbstract
Landslides are one of the most destructive natural hazard in the mountainous region, frequently triggered by intense or prolonged rainfall during monsoon season. The Gorkha District of central Nepal is particularly exposed to landslides due to its fragile geology, steep slopes and enhanced susceptibility following 2015 Gorkha Earthquake. This research aims to establish empirical rainfall threshold for landslide initiation in the district and evaluate their applicability for early warning system. The intensity Duration (ID) relationship was derived using Ordinary Least Squares (OLS) regression and refined through CTRL-T algorithm to ensure that the majority of landslide lie above the threshold. To quantify uncertainty, bootstrap prediction bands were generated. The final threshold equation, I= 47.26D^−0.706, shows the dominant rainfall condition responsible for the triggering landslide in the study area. To establish the relationship between landslide and other slope factor, land use and rainfall parameter Random Forest classifier was used. A residual bootstrap technique was used to construct 90% prediction band around the fitted ID curve which represented the expected variability of future landslide triggering events. Results shows that the short duration high intensity rainfall is primarily trigger for shallow landslide while prolonged moderated rainfall contributes to deeper failure.
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