Modeling and simulation of the effects of landslide on circulation of transports on the mountain roads

Nguyễn, Mạnh Hùng; Ho, Tuong Vinh; Nguyen-Trong, Khanh; M, Duc

doi:10.14569/ijacsa.2015.060835

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…8) inevitably become areas with greater changes in land use and further development of infrastructure, as obvious from the higher density of the road network (Meyer and Turner 1992). Similar factors have been identified widely in the literature as drivers of increased landslide susceptibility in mountain areas (e.g., Hung et al 2015;Brenning et al 2015). The particular geological characteristics of areas 12, 13, and 14 likely add in a decisive way to their susceptibility to landslide activity (Online Resource 4).…”

Section: Correlations Between Landslide Occurrences and Climate Variamentioning

confidence: 72%

Three decades of landslide activity in western Nepal: new insights into trends and climate drivers

et al. 2021

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In recent decades, landslide disasters in the Himalayas, as in other mountain regions, are widely reported to have increased. While some studies have suggested a link to increasing heavy rainfall under a warmer climate, others pointed to anthropogenic influences on slope stability, and increasing exposure of people and assets located in harm’s way. A lack of sufficiently high-resolution regional landslide inventories, both spatially and temporally, has prevented any robust consensus so far. Focusing on Far-Western Nepal, we draw on remote sensing techniques to create a regional inventory of 26,350 single landslide events, of which 8778 date to the period 1992–2018. These events serve as a basis for the analyses of landslide frequency relationships and trends in relation to precipitation and temperature datasets. Results show a strong correlation between the annual number of shallow landslides and the accumulated monsoon precipitation (r = 0.74). Furthermore, warm and dry monsoons followed by especially rainy monsoons produce the highest incidence of shallow landslides (r = 0.77). However, we find strong spatial variability in the strength of these relationships, which is linked to recent demographic development in the region. This highlights the role of anthropogenic drivers, and in particular road cutting and land-use change, in amplifying the seasonal monsoon influence on slope stability. In parallel, the absence of any long-term trends in landslide activity, despite widely reported increase in landslide disasters, points strongly to increasing exposure of people and infrastructure as the main driver of landslide disasters in this region of Nepal. By contrast, no climate change signal is evident from the data.

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Section: Correlations Between Landslide Occurrences and Climate Variamentioning

confidence: 72%

Three decades of landslide activity in western Nepal: new insights into trends and climate drivers

et al. 2021

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Section: Original Papermentioning

confidence: 72%

Three decades of landslide activity in western Nepal: New insights into trends and climate drivers

Manchado¹,

Allen²,

Ballesteros‐Cánovas³

et al. 2021

Preprint

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Landslide activity in the Himalaya region is hypothesized to have increased over the last decades, as suggested by exiting landslide databases and disaster inventories. This trend has been linked to an enhancement of heavy rainfall events under warming climate, but also to anthropogenic factors that influences the slope stability as well as to an increase of exposed of people and infrastructures in prone areas. Yet, as recognized by the Intergovernmental Panel on Climate Change (IPCC), such positive trends are still unclear, mostly due to the lack of baseline data with enough spatio-temporal resolution. Focusing on Far-Western Nepal, we draw on remote sensing techniques to create a multi-temporal regional landslide inventory for the period 1992-2018 over an area covering 6,460 km2. To this end, we systematically interpret geomorphologically high-resolution satellite imagery from Google Earth. Besides, we analyze multispectral differences from Landsat images to interannual date the initiation or reactivation of the interpreted landslides. This massive effort includes the digitalization of 26,350 landslide events, of which 8,778 were dated at an annual scale. These events serve as a basis for the analyses of landslide frequency relationships and trends in relation to annual precipitation and temperature datasets, derived from ERA-5 climate reanalysis. Our results show a strong correlation between the annual number of shallow landslides and the accumulated monsoon precipitation (r=0.74). Furthermore, warm and dry monsoons followed by especially rainy monsoons produce the highest incidence of shallow landslides (r=0.77). However, we find strong spatial variability in the strength of these relationships, which is linked to recent demographic development in the region. This highlights the role of anthropogenic drivers, and in particular, road cutting and land-use change, in amplifying the seasonal monsoon influence on slope stability. In parallel, the absence of any long-term trends in landslide activity, despite a widely reported increase in landslide disasters, points strongly to increasing exposure of people and infrastructure as the main driver of landslide disasters in this region of Nepal. Thus, our assessment could not determine evidence for any climate change signal related to landslide activity over this part of the Himalayas.

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“…These natural factors combine with anthropogenic processes such as deforestation, especially at the upstream areas, can cause grave hazards, for example landslide. When it happens, roads are blocked, which causes hurdles not only in the traffic flow but generate various traffic problems in the form of congestion [1]. Sometimes this kind of disaster can lead to the risk of human life because there are people who got stuck between damaged roads.…”

Section: Introductionmentioning

confidence: 99%

Landslide Detection with Unmanned Aerial Vehicles

Vu¹,

Nguyen

Pham

et al. 2021

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)

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Landslide is one of the most dangerous disasters, especially for countries with large mountainous terrain. It causes a great damage to lives, infrastructure and environments, such as traffic congestion and high accidents. Therefore, automated landslide detection is an important task for warning and reducing its consequences such as blocked traffic or traffic accidents. For instance, people approaching the disaster area can adjust their routes to avoid blocked roads, or dangerous traffic signs can be positioned in time to warn the traffic participants to avoid the interrupted road ahead. This paper proposes a method to detect blocked roads caused by landslide by utilizing images captured from Unmanned Aerial Vehicles (UAV). The proposed method comprises of three components: road segmentation, blocked road candidate extraction, and blocked road classification, which is leveraged by a multi-stage convolutional neural network model. Our experiments demonstrate that the proposed method can surpass over several state-of-the art methods on our self-collected dataset of 400 images captured with an UAV.

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Modeling and simulation of the effects of landslide on circulation of transports on the mountain roads

Cited by 6 publications

References 25 publications

Three decades of landslide activity in western Nepal: new insights into trends and climate drivers

Three decades of landslide activity in western Nepal: new insights into trends and climate drivers

Three decades of landslide activity in western Nepal: New insights into trends and climate drivers

Landslide Detection with Unmanned Aerial Vehicles

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