Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment

Abstract

Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technology, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned.

Artificial Intelligence (AI) Applied in Civil Engineering

“…The method can be used to invert the geological parameters of the surrounding rock mass for a certain point, which can provide important mechanical parameters for the design and construction of tunnels. Lin et al [36] introduce a modern space remote sensing technology, InSAR, as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors are integrated, and their correlations with landslide susceptibility are analyzed.…”

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

“…In recent years, a few studies became to focus on the influence of surface deformation on landslide susceptibility and employed deformation features (derived by InSAR technique) to validate or refine the LSE results acquired from historical landslides (e.g., Xie et al, 2017;Zhao et al, 2019;Hussain et al, 2020;Hussain M.A. et al, 2021;Hussain S. et al, 2021;Lin et al, 2021;Meghanadh et al, 2021;Cao et al, 2022;Meghanadh et al, 2022). These studies actually concerned the known landslides.…”

Landslide susceptibility mapping based on the deformation intensity

“…Landslide susceptibility prediction mainly aims to predict the possibility of developing landslides in a specific area (Lin et al, 2021). The susceptibility analysis using static factors (slope, aspect, road distance, etc.)…”

“…At present, many scholars focus on topography, hydrology, geology, and human activities as the main factors when modeling landslide susceptibility. However, with the continuous promotion and application of InSAR technology, some scholars have begun to try using InSAR interpretation results as an evaluation factor for landslide susceptibility and achieved excellent evaluation results (Lin et al, 2021;Yao et al, 2023). However, the study of landslide susceptibility under different deformation intensities is still insufficient.…”