Search Results (2)

In this study, landslide risk assessment was conducted in the Renhe District, Panzhihua City, China. Firstly, based on 190 landslide points and 10 influencing factors, the landslide hazard was assessed using three models: random forest (RF), eXtreme Gradient Boosting (XGBoost), and Tabular Prior-data Fitted Network (TabPFN). The results indicate that the RF and XGBoost models exhibit comparable performance, both demonstrating strong generalization and accuracy, with the RF model achieving superior generalization, as evidenced by an area-under-the-curve (AUC) value of 0.9471. While the AUC value of TabPFN is 0.9243, indicating higher accuracy, it also poses a risk of overfitting and is therefore more suitable for applications involving small sample sizes and the need for rapid responses. The vulnerability assessment utilized the Analytic Hierarchy Process (AHP) to determine the weights of four disaster-bearing bodies, with sensitivity analysis revealing that road type was the most sensitive vulnerability factor. Finally, the landslide risk-assessment map of the Renhe District was produced by integrating the landslide hazard assessment map with the vulnerability assessment map. The findings indicate that the high-risk zones comprised 2.08% of the research region, which includes three principal train stations and necessitates enhanced protective measures. The medium-risk zones comprise 34.23% of the total area and are scattered throughout the region. It is important to enhance local capabilities for landslide monitoring and early warning systems. Relevant conclusions can provide a significant reference for landslide disaster prevention and mitigation work in the Renhe District and help ensure the safe operation of public transport infrastructure, such as railway stations and airports in the district. Full article

Tropospheric delays in spaceborne Interferometric Synthetic Aperture Radar (InSAR) can contaminate the measurement of small amplitude earth surface deformation. In this paper, a novel TXY-correlated method is proposed, where the main tropospheric delay components are jointly modeled in three dimensions, and then the long-scale and topography-correlated tropospheric delay components are corrected simultaneously. Moreover, the strategies of scale filtering and alternative iteration are employed to accurately retrieve all components of the joint model. Both the TXY-correlated method and the conventional phase-based methods are tested with a total of 25 TerraSAR-X/TanDEM-X images collected over the Chaobai River site and the Renhe Town of Beijing Shunyi District, where natural scenes and man-made targets are contained. A higher correction rate of tropospheric delays and a greater reduction in spatio-temporal standard deviations of time series displacement are observed after delay correction by the TXY-correlated method in both non-urban and urban areas, which demonstrate the superior performance of the proposed method. Full article