Co-Seismic Landslide Mobility Assessment Using Machine Learning Models
Publication: Geo-Congress 2024
ABSTRACT
Landslide mobility is essential for landslide risk assessment because impact scales with the distance traveled by the landslide mass in many cases. This paper aims to quantify the mobility of landslides triggered by the Mw 6.5 Lefkada earthquake on November 17, 2015, and develop regression models for landslide travel distance. The study leverages a high-quality landslide inventory that includes the location, area, and volume of 716 landslides, with the source and entire area for each landslide mapped separately. Multivariate linear and machine learning models are used to predict landslide travel distance. The dependent variable, the 3D landslide travel distance, is calculated from the inventory with the digital elevation model. Independent variables include terrain characteristics, material strength, and permanent seismic displacements estimated from seismic displacement models. The results show that terrain characteristics correlate most strongly with landslide travel distances. Furthermore, the multivariate linear regression, random forest regression, and stochastic gradient descent regression have better prediction capacity than k-nearest neighbors’ and XGBoost regression.
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Information & Authors
Information
Published In
Geo-Congress 2024
Pages: 475 - 484
History
Published online: Feb 22, 2024
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Freight transportation
- Geography
- Geohazards
- Geomatics
- Geotechnical engineering
- Infrastructure
- Inventories
- Landslides
- Linear functions
- Logistics
- Material mechanics
- Material properties
- Materials engineering
- Mathematical functions
- Mathematics
- Models (by type)
- Regression analysis
- Statistical analysis (by type)
- Strength of materials
- Terrain
- Three-dimensional models
- Transportation engineering
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