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Landslide Susceptibility Assessment Using Earthquake Ground Motion for Different Return Periods in Rasuwa District, Central Nepal

Description: 

This study in Rasuwa district focuses on characterizing co-seismic landslide-prone areas by integrating peak ground acceleration (PGA) data expected for various earthquake return periods into a composite map that combines seismic hazard and landslide susceptibility mapping (LSM). Probabilistic seismic hazard assessment (PSHA) is employed to generate seismic hazard maps, depicting PGA for 50%, 10%, 5%, and 2% probability of exceedance (PE) over 50 years. Notably, hazard levels increase from the northern to southern parts of the study area, with the highest PGAs recorded at 0.19g, 0.35g, 0.45g, and 0.55g, respectively. Simultaneously, logistic regression (LR) is utilized for LSM, analyzing 890 co-seismic landslides with 23 different conditioning factors (CF). Standardization of continuous and categorical CF variables is conducted for the coefficients of each factor map. The relative relief emerges as the most significant predictor among the various factors, and the LSM results are classified into five susceptibility indexes. High susceptibility areas concentrate along both banks of the Trishuli River, the main drainage system in the southern part of the study area.

Validation of the LSM model is carried out using the area under the ROC curve and statistical inferences method, demonstrating good agreement with previous landslide occurrences. Composite maps reveal an increase in susceptible areas from 50% to 2% PE in 50 years, with high and very high susceptibility indexes increasing sequentially with rising PGA. The integration of seismic hazard maps and LSM offers a comprehensive understanding of co-seismic landslide-prone zones over different timeframes, providing valuable insights for development planning. These results hold practical implications for formulating and revising building code provisions, implementing slope improvement measures, guiding generalized land use planning, and informing subsequent management strategies.

Session: Detecting, Characterizing and Monitoring Mass Movements [Poster Session]

Type: Poster

Date: 5/2/2024

Presentation Time: 08:00 AM (local time)

Presenting Author: Manoj

Student Presenter: Yes

Invited Presentation: 

Authors