Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

Abstract

Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.

A Strategy for Neighboring Pixel Collaboration in Landslide Susceptibility Prediction

“…Potential side-slope excavations and the earth's surface vibrations caused by vehicle transportation on roadways are additional potential factors influencing landslides. Existing studies suggest a centered landslide distribution along roadways and an inverse relationship between landslide density and distance to roads [39].…”

Hazard assessment of seismic-collapsed loess landslides on the Loess Plateau as exemplified by the M6.2 earthquake in Jishishan County, China

“…The morphology of seismic-collapsed loess landslides is strongly influenced by surface reconstruction, and it is difficult to obtain the number and characteristics of this landslide type through remote sensing interpretation and inversion analysis (Fang et al, 2023). Currently, there is no complete database of seismic-collapsed loess landslides, and data-driven statistical analyses and machine-learning methods with high requirements for sample set construction are not applicable for the time being (Shahabi et al, 2023;Zhang et al, 2024b;Li et al, 2024). The study selected the minimum hazard-causing seismic peak ground acceleration zoning method combined with the analytic hierarchy process.…”