Landslide Susceptibility Analysis Using Numerical and Neural Network, Near Kedarnath, Uttarakhand, India

The major concern in hilly regions is the stability of those slopes, which have been proclaimed due to unplanned excavation and uneven blasting during road widening and development activity. These slopes again become more vulnerable under dynamic loading and/or various types of human involvement, heavy rainfall and seismic activity. Failure of these slopes leads to loss of property and human being, disruption of traffics and environmental degradation. The Kedarnath area is the most vulnerable hilly terrain due to its inferred locality. To analyze the vulnerability near Kedarnath, the field observation was done to collect the geological and geotechnical details of three vulnerable locations. The present article illustrates the collective analysis of numerical simulation and artificial intelligence (ANN) models for the chosen vulnerable soil slopes. Numerical modeling was done to compute safety factor, stress distribution and maximum displacement using LEM and FEM modules. Further, the machine learning technique, ANN was also functionate to predict the stability based on geotechnical data’s and numerical simulation results. The numerical analysis for the homogenous finite slopes shows that slopes are stable, critically stable and also prone to failure during rainy season. The ANN model evaluate that, the FoS by numerical modeling displays 98% validation to predictive neural networking system. The simulation result could be effectively applied to lessen/decrease the effect of regularity for the landslides in the area of particular morphology.