Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation

She, Xiangjie; Li, Deying; Yang, Shuo; Xie, Xiaoxu; Sun, Yiqing; Zhao, Wenjie

doi:10.3390/rs16091587

Cited by 2 publications

References 42 publications

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Identification of Potential Landslide in Jianzha Counctry Based on InSAR and Deep Learning

Yang,

Chen,

Dong

et al. 2024

Preprint

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Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR、SBAS-InSAR and optical remote sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. The results indicate that: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, it was found that 10 out of 13 previously occurred landslide disaster points were located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10–40°, elevations between 15–30 m, and slope orientations predominantly towards the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.

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Identification of Potential Landslide in Jianzha Counctry Based on InSAR and Deep Learning

Yang,

Chen,

Dong

et al. 2024

Preprint

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Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models

Chang,

Zhao,

Zhou

et al. 2024

Sensors

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Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east–west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.

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Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation

Cited by 2 publications

References 42 publications

Identification of Potential Landslide in Jianzha Counctry Based on InSAR and Deep Learning

Identification of Potential Landslide in Jianzha Counctry Based on InSAR and Deep Learning

Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models

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