Geological Hazard Risk Assessment Based on Time-Series InSAR Deformation: A Case Study of Xiaojin County, China
Source abstract
Abstract
Geological hazard risk assessment provides essential scientific support for geological disaster prevention and governance. The selection of appropriate evaluation factors is crucial to the accuracy and practicality of the risk assessment results. The existing factors for geological hazard risk assessment often suffer from issues such as poor timeliness and insufficient completeness. Interferometric Synthetic Aperture Radar (InSAR) technology, which offers large-scale, high spatiotemporal resolution monitoring of surface deformation, can effectively compensate for the shortcomings of existing risk assessment factors. How to effectively integrate time-series InSAR deformation results into geological hazard risk assessment has become a focus of research. This study fully considers the time-series InSAR deformation information; both the ascending and descending orbit results of the time-series InSAR deformation are introduced as two categories of evaluation factors in the risk assessment model. Subsequently, 11 types of assessment factors are selected by the Pearson correlation coefficient method, while the Information Volume Model and Evidence Weight Model are applied in the partitioning and assessment of risks in Xiaojin County, China. Finally, ROC (Receiver Operating Characteristic Curve) analysis is utilized to compare the accuracy of model evaluations before and after incorporating time-series InSAR deformation results. The results indicate that: (1) after incorporating time-series InSAR deformation monitoring results as evaluation factors into the information volume model and evidence weight model, the evaluation accuracy of the two models improved by 9.69% and 11.26%, respectively; (2) there are differences in risk partitioning among different evaluation models. From the risk partitioning result of Xiaojin County in this study, the evaluation accuracy of the information volume model is higher than that of the evidence weight model, and the performance is more prominent after adding the time-series InSAR deformation results.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Synthetic Aperture Radar (SAR) Applications and Techniques · Landslides and related hazards · Earthquake Detection and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
China Areo Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
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