Journal Article

Geological Hazard Identification and Susceptibility Assessment Based on MT-InSAR

Lu, Zhaowei, Zeng, Wei, Liu, Peng, Yang, Honglei, Wang, Yuedong

2023

Overview

Geological hazards often occur in mountainous areas and are sudden and hidden, so it is important to identify and assess geological hazards. In this paper, the western mountainous area of Beijing was selected as the study area. We conducted research on landslides, collapses, and unstable slopes in the study area. The surface deformation of the study area was monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR), using a combination of multi-looking point selection and permanent scatterer (PS) point selection methods. Random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) models were selected for the assessment of geological hazard susceptibility. Sixteen geological hazard-influencing factors were collected, and their information values were calculated using their features. Multicollinearity analysis with the relief-F method was used to calculate the correlation and importance of the factors for factor selection. The results show that the deformation rate along the line-of-sight (LOS) direction is between −44 mm/year and 28 mm/year. A total of 60 geological hazards were identified by combining surface deformation with optical imagery and other data, including 7 collapses, 25 unstable slopes, and 28 landslides. Forty-eight of the identified geological hazards are not recorded in the Beijing geological hazards list. The most effective model in the study area was RF. The percentage of geological hazard susceptibility zoning in the study area is as follows: very low susceptibility 27.40%, low susceptibility 28.06%, moderate susceptibility 21.19%, high susceptibility 13.80%, very high susceptibility 9.57%.

• Publisher: MDPI AG
• Subject: Artificial intelligence / Artificial neural networks / Beijing western mountain / China / Collapse / Comparative analysis / deep learning / Deformation / Environmental aspects / geological hazard identification / geological hazard susceptibility assessment / Geological hazards / Geology / Hazard assessment / Hazard identification / Identification / Interferometric synthetic aperture radar / interferometry / Landslides / Landslides & mudslides / Line of sight / Machine learning / Mountain regions / Mountainous areas / Mountains / MT-InSAR / Neural networks / Observations / Precipitation / Recurrent neural networks / Slope stability / Stratigraphy / Support vector machines / Susceptibility / Synthetic aperture radar / Topography