Risk assessment of roadway networks exposed to landslides in mountainous regions—a case study in Fengjie County, China

Zhang, Y.; Ayyub, Bilal M.; Gong, Wenping; Tang, Huiming

doi:10.1007/s10346-023-02045-3

Cited by 17 publications

(4 citation statements)

References 49 publications

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“…At first, we did not rule out available or main factors that are easy to deduce, as these factors may help explain landslide susceptibility. These factors have been used in many other studies, and descriptions of these factors can be found in a large number of studies [2,11,19,[39][40][41][42]; so, this article did not elaborate upon this further. In Table 1, we briefly explained the significance of these factors in LSA.…”

Section: Factor Datamentioning

confidence: 99%

Modeling and Assessment of Landslide Susceptibility of Dianchi Lake Watershed in Yunnan Plateau

Bai,

Yang,

Kong

et al. 2023

Sustainability

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The nine plateau lake watersheds in Yunnan are important ecological security barriers in the southwest of China. The prevention and control of landslides are important considerations in the management of these watersheds. Taking the Dianchi Lake watershed as a typical research area, a comprehensive modeling and assessment process of landslide susceptibility was put forward. The comprehensive process was based on the weight of evidence (WoE) method, and many statistical techniques were integrated, such as cross-validation, multi-quantile cumulative Student’s comprehensive weight statistics, independence testing, step-by-step modeling, ROC analysis, and ROC-based susceptibility zoning. In this paper, fourteen models with high accuracy and validity were established, and the AUC reached 0.83–0.87 and 0.85–0.88, respectively. In addition, according to the susceptibility zoning map compiled via the optimal model, 80% of landslides can be predicted in the very-high- and high-susceptibility areas, which only account for 19.58% of the study area. Finally, this paper puts forward strategies for geological disaster prevention and ecological restoration deployment.

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Section: Factor Datamentioning

confidence: 99%

Modeling and Assessment of Landslide Susceptibility of Dianchi Lake Watershed in Yunnan Plateau

Bai,

Yang,

Kong

et al. 2023

Sustainability

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show abstract

“…Such analyses have been conducted through theoretical investigations, on-site inspections, and expert scoring methodologies [9][10][11][12][13][14][15]. Leveraging GIS technology, several experts have conducted spatiotemporal distribution studies and risk assessments involving highway incidents such as rockfalls [16][17][18], debris flows [19][20][21], and landslides [22][23][24]. Based on such research, targeted mitigation measures have been proposed [25,26].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Spatio-Temporal Trends of Geomorphological Incidents Induced by Precipitation on Chinese Highways

Zhang,

Tian,

Song

2024

Atmosphere

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The spatiotemporal distribution of geomorphological incidents was examined in the present study, including the characteristics of obstruction distances and durations, by utilizing nationwide incident mitigation data and precipitation observation records from the period spanning 2019 to 2022. By comparing rainfall features over different temporal scales across various regions, the aim of the present study was to enhance the current comprehension of the patterns through which regional precipitation initiates incidents on highways by comparing rainfall characteristics over distinct temporal scales across diverse geographical areas. The findings indicate that: (1) The spatial distribution of highway incidents in China is significantly correlated with regional natural environments, predominantly concentrated in the southern parts of the country’s second and third topographical terraces. The temporal distribution closely aligns with annual and monthly precipitation patterns, with the majority of occurrences taking place from June to September. Further, notable disparities in the distribution of highway-related incidents were observed among counties across most provinces; (2) National highways experience a notably higher frequency of incidents than expressways and provincial roads, with most obstruction lengths concentrated within 1 km and durations predominantly under 3 days; (3) The probability of daily rainfall inducing highway incidents is distinctly higher than that of short-duration rainfall, with eastern and southern China experiencing significantly greater inducing precipitation volumes than other regions. The majority of areas are susceptible to incidents within a 3-day window following heavy rainfall or within 24 h after intense short-duration rainfall. Moreover, it is observed that incidents are more closely associated with extreme precipitation occurring within a single day; (4) There is a certain lag between the timing of incidents and the occurrence of extreme short-duration heavy rainfall, with the highest frequency of incidents coinciding with continuous rainfall periods of 3 to 6 days.

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“…These studies are categorized based on the triggering factors of landslides [5], primarily including those induced by rainfall [6][7][8] and earthquakes [9][10][11]. Key research topics in landslides include the prediction of displacement in single landslide [9,12], identification of landslides [13][14][15], susceptibility assessment [2,16], hazard evaluation [17], and risk assessment [18,19].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Landslide Detection with SBConv-Optimized U-Net Architecture Based on Multisource Remote Sensing Data

Song,

Zou,

et al. 2024

Land

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This study introduces a novel approach to landslide detection by incorporating the Spatial and Band Refinement Convolution (SBConv) module into the U-Net architecture, to extract features more efficiently. The original U-Net architecture employs convolutional layers for feature extraction, during which it may capture some redundant or less relevant features. Although this approach aids in building rich feature representations, it can also lead to an increased consumption of computational resources. To tackle this challenge, we propose the SBConv module, an efficient convolutional unit designed to reduce redundant computing and enhance representative feature learning. SBConv consists of two key components: the Spatial Refined Unit (SRU) and the Band Refined Unit (BRU). The SRU adopts a separate-and-reconstruct approach to mitigate spatial redundancy, while the BRU employs a split-transform-and-fuse strategy to decrease band redundancy. Empirical evaluation reveals that models equipped with SBConv not only show a reduction in redundant features but also achieve significant improvements in performance metrics. Notably, SBConv-embedded models demonstrate a marked increase in Recall and F1 Score, outperforming the standard U-Net model. For instance, the SBConvU-Net variant achieves a Recall of 75.74% and an F1 Score of 73.89%, while the SBConvResU-Net records a Recall of 70.98% and an F1 Score of 73.78%, compared to the standard U-Net’s Recall of 60.59% and F1 Score of 70.91%, and the ResU-Net’s Recall of 54.75% and F1 Score of 66.86%. These enhancements in detection accuracy underscore the efficacy of the SBConv module in refining the capabilities of U-Net architectures for landslide detection of multisource remote sensing data. This research contributes to the field of landslide detection based on remote sensing technology, providing a more effective and efficient solution. It highlights the potential of the improved U-Net architecture in environmental monitoring and also provides assistance in disaster prevention and mitigation efforts.

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Risk assessment of roadway networks exposed to landslides in mountainous regions—a case study in Fengjie County, China

Cited by 17 publications

References 49 publications

Modeling and Assessment of Landslide Susceptibility of Dianchi Lake Watershed in Yunnan Plateau

Modeling and Assessment of Landslide Susceptibility of Dianchi Lake Watershed in Yunnan Plateau

Exploring the Spatio-Temporal Trends of Geomorphological Incidents Induced by Precipitation on Chinese Highways

Enhancing Landslide Detection with SBConv-Optimized U-Net Architecture Based on Multisource Remote Sensing Data

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