Identification and susceptibility assessment of landslides along railway lines using MPSO-RF considering INSAR deformation
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| Publicado en: | Journal of Engineering and Applied Science vol. 72, no. 1 (Dec 2025), p. 264 |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1110-1903 | ||
| 022 | |a 1110-1393 | ||
| 024 | 7 | |a 10.1186/s44147-025-00806-6 |2 doi | |
| 035 | |a 3286147123 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Guo, Rongchang |u Lanzhou Jiaotong University, College of Automation and Electrical Engineering, Lanzhou, China (GRID:grid.411290.f) (ISNI:0000 0000 9533 0029) | |
| 245 | 1 | |a Identification and susceptibility assessment of landslides along railway lines using MPSO-RF considering INSAR deformation | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the continuous promotion of railway construction in China, railway lines are increasingly extended to areas with complex geological environment, and such areas are prone to landslides and other geological disasters, which seriously threaten the safety of railway operation. The current landslide susceptibility assessment along the railway line relies on static factors such as topography and geology, and fails to take into account the significant time-varying and sudden nature of landslide disasters in complex geological environments, This poses a challenge in terms of satisfying the actual demand for dynamic perception of landslide hazards, and to reflect the deformation characteristics of potential landslides. For this reason, this paper utilizes to introduce the Interferometric Synthetic Aperture Radar (InSAR) technique to dynamically extract the surface deformation characteristics, as an effective supplement to the existing static factors, to enhance the promptness and precision of landslide susceptibility evaluation. Firstly, INSAR was used to obtain surface deformation in the study area and combined with optical remote sensing to identify landslides. Secondly, the deformation rate was taken as a dynamic factor, and 12 static factors, such as elevation and rainfall, were combined to construct a Mean Particle Swarm Optimisation -Random Forest (MPSO-RF) model, and the dynamic factors were introduced into the model through joint training and weighted superposition and performed. accuracy comparison and landslide susceptibility evaluation. Finally, the causes of landslides were analysed by combining the results of INSAR identification and model evaluation. The results show that: (1) the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique can effectively identify potential landslide areas in slow deformation; (2) the accuracy of the joint training and weighted superposition models is improved by 6.54% and 3%, respectively, compared with that of the static model subsequent to the introduction of the INSAR deformation data; (3) the joint evaluation of the SBAS-InSAR and the MPSO-RF model can effectively supplement the traditional static evaluation with the lack of dynamic information. evaluation with the lack of dynamic information. The results of the study can provide theoretical basis and methodological support for the construction of line safety environment platform in railway disaster prevention and monitoring system. | |
| 651 | 4 | |a China | |
| 653 | |a Particle swarm optimization | ||
| 653 | |a Accuracy | ||
| 653 | |a Geology | ||
| 653 | |a Rainfall | ||
| 653 | |a Topography | ||
| 653 | |a Human engineering | ||
| 653 | |a Remote sensing | ||
| 653 | |a Data processing | ||
| 653 | |a Deformation effects | ||
| 653 | |a Hazard assessment | ||
| 653 | |a Disasters | ||
| 653 | |a Monitoring systems | ||
| 653 | |a Railway construction | ||
| 653 | |a Interferometry | ||
| 653 | |a Interferometric synthetic aperture radar | ||
| 653 | |a Machine learning | ||
| 653 | |a Precipitation | ||
| 653 | |a Landslides & mudslides | ||
| 653 | |a Static models | ||
| 653 | |a Earthquakes | ||
| 653 | |a Geological hazards | ||
| 653 | |a Algorithms | ||
| 653 | |a Spatial data | ||
| 653 | |a Deformation | ||
| 653 | |a Rivers | ||
| 653 | |a Landslides | ||
| 700 | 1 | |a Zhang, Shanghuan |u Lanzhou Jiaotong University, College of Automation and Electrical Engineering, Lanzhou, China (GRID:grid.411290.f) (ISNI:0000 0000 9533 0029) | |
| 773 | 0 | |t Journal of Engineering and Applied Science |g vol. 72, no. 1 (Dec 2025), p. 264 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286147123/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3286147123/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286147123/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |