Regional and Local-scale Analysis of Landslides Induced by Rainfall and Earthquakes

Abstract

Landslides are major natural disasters which pose a significant risk to lives and infrastructure globally. As urbanization is increasing due to the increasing population in mountainous regions, the risk due to landslides draws grave concern owing to the damage and disruption since the last decade. Hence, regional-scale and local-scale landslide analyses are necessary to reduce the impact of landslides on lives and infrastructure; and efficiently prevent the landslide risk. The landslide analysis must be conducted separately for different triggering factors as the slope materials follow different failure mechanisms under various triggering factors. In this thesis, efficient models for landslide analysis at regional-scale as well as local-scale are developed, focusing mainly on understanding the relationship between actuating factors and slope failure events; and the slope failure mechanism. These models are developed for different causal factors, including rainfall and earthquakes. For regional scale analysis of landslides, a methodology is introduced for landslide mapping, which aims at the accurate and faster demarcation of slope areas affected by landslides. Fast and accurate landslide mapping forms the basis of research and practice of landslide hazard and risk analysis. A systematic framework is also presented to estimate landslide hazard at regional-scale using previous landslide incidents and establish a relationship between different triggering factors and landslide incidents. Further, a predictive model is proposed to estimate the evolution of seismically induced slope displacement with time. The developed model is based on the dynamic response surface method (DRSM). Various methodologies are proposed for local-scale analysis of slope systems under various causal factors, i.e., rainfall and earthquakes to estimate the uncertainty in soil parameters using probabilistic methods and machine learning algorithms. Several algorithms are developed and implemented in Python and MATLAB to add new features that introduce complexity in the numerical models and interface the deterministic and probabilistic analysis. Overall, it is anticipated that the work presented in this thesis will facilitate guidelines for 1) landslide inventory and hazard mapping due to rainfall infiltration, 2) estimation of evolution of seismically-induced slope displacement with time using predictive models, and 3) probabilistic back analysis of slope system under various causal factors.