Co-seismic landslide susceptibility analysis for the Bhagirathi valley of Uttarakhand Himalayan region using machine learning algorithms based on Slope unit techniques

High-magnitude earthquakes are often in seismic zones that initiate the cascading chain of hazards such as co-seismic landslides, soil liquefaction, snow avalanche, surface faulting, devastating rock avalanches, and ground shaking. In the present study, a co-seismic landslide susceptibility analysis was executed for the Bhagirathi valley of Uttarakhand Himalayan region using machine learning techniques based on the slope unit-based method. The study area falls in seismic zone IV, rocks along the fault zone are fragile, and this area is very active seismically. This region has previously experienced Uttarkashi earthquake (1991) of magnitude 6.6. Assessment of seismic induced landslide is considered a complex process, as it considers both static parameters (causative factors) and dynamic parameters (triggering factor) in the form of ground motion shaking effects. In this study, the co-seismic landslide susceptibility maps using the machine learning approach Extreme Gradient Boosting (XgBoost) and Naïve Bayes (NB) techniques have been carried out at Slope Unit-based mapping level. The landslide inventory with 3,000 delineated polygons has been classified into training (80%) and testing (20%) data to calibrate and authenticate the models. For this purpose, static causative factors have been considered, such as slope, aspect, curvature, lineament buffer, drainage buffer, geology, topographic wetness index, and normalized difference vegetation index (NDVI), these parameters have been generated using the CartoDEM and satellite data. Triggering factors Arias Intensity (AI) has been considered for ground motion shaking as a dynamic factor for co-seismic landslides susceptibility mapping. Arias Intensity was prepared using the classical Cornell approach by considering the earthquake catalogue between the years 1700 and 2022. Finally, XgBoost and NB techniques have been used to compute static landslide susceptibility mapping and dynamic co-seismic landslide susceptibility map for a 475-year return period. XgBoost methods at the slope unit level predicted better results. These results were validated using the seismic relative index (SRI) and landslide density method. The prepared map can be effectively helpful for local and regional planning.

 

Keywords: Co-seismic landslide, Slope Unit, Landslide mapping, Machine learning.