Landslides and dam damage resulting from the Jiuzhaigou earthquake (8 August 2017), Sichuan, China

Zhao, Bo; Wang, Yunsheng; Luo, Yonghong; Li, Jia; Shen, Tong

doi:10.1098/rsos.171418

Cited by 49 publications

(26 citation statements)

References 30 publications

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“…The present study observed that the triggering of the landslides is in direct correlation with the distance from the drainage valley Zhao et al 2018). Figure 9 shows that maximum numbers (303) of landslides have occurred within 200 m from the drainage valley beds; this zone covers the maximum landslide frequency rate (53.2%).…”

Section: Distance From the Drainage Linesmentioning

confidence: 51%

GIS based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India

Koley¹,

Nath²,

Bhattacharya³

et al. 2020

Preprint

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Landslide Hazards Zonation Mapping is a major tool for the geographer, geologist, ground engineer, and land-use planner for landslide prevention strategies. The main outcome of the present study is to prepare a Landslide Hazards Zonation Map of the region along the North Sikkim Road Corridor in Sikkim Himalayas, an area highly vulnerable to the landslides. The initial step of this study is involved preparation of input raster layers of the landslide controlling factors. Seven controlling factors are selected for this purpose. These controlling factor-like slope, aspect, lithology, faults, river alignment, road network, and land-use are through Geographic Information System software using multi-criteria analysis. The Analytic Hierarchy Process is used to determine the weightage of the various causative factors. Weighted Overlay Method is used for the assignment of ranks and weights to each factor. The Landslide Hazard Zonation Index is then estimated with the help of a multi-criteria analysis based on assigned rank and weight given by the Analytic Hierarchy Process. Finally, Landslide Hazard Zonation Mapping is done along the study area road corridor. Based on Hazards Index, the study area is classified into four hazard zones and classified as Very High (12.12%), High (40%), Moderate (37.20%), and Low Hazard Zones (10.68%). This zonation map is helpful for landslide hazard prevention, mitigation, proper planning of tourism and land-use management, and social development along the North Sikkim Road Corridor.

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Section: Distance From the Drainage Linesmentioning

confidence: 51%

GIS based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India

Koley¹,

Nath²,

Bhattacharya³

et al. 2020

Preprint

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“…The occurrence of landslides is a consequence of geological, meteorological, anthropogenic and triggering factors, commonly referred to as landslide controlling factors (Bai et al, 2010). Standard guidelines for choosing the optimal landslide controlling factors are unavailable, but the scale of analysis, the nature of the study area, the data availability, and the quasi-empirical and statistical criterions in literature can be referenced (Romer and Ferentinou, 2016;Zhou et al, 2016). In this study, slope, aspect, elevation, lithology, distance from faults, distance from rivers, land use-land cover (LULC), normalized difference vegetation index (NDVI), and peak ground acceleration (PGA) were selected as the landslide controlling factors, as shown in Fig.…”

Section: Landslide Controlling Factorsmentioning

confidence: 99%

“…1), and this earthquake was caused by tectonic movement of an NW-SE-oriented leftlateral strike-slip fault (Wang et al, 2018a). Although intense rainfall was not observed after the earthquake, numerous landslides were triggered by strong seismic vibration of ground (Zhao et al, 2018). Many scenic spots in the Jiuzhaigou National Nature Reserve were destroyed; as presented in Fig.…”

mentioning

confidence: 98%

“…2d, the S301 highway was severely obstructed by a significant number of small-scale landslides. Based on field investigation, most of these landslides were small-scale rockslides, rockfalls and debris slides (Fan et al, 2018;Zhao et al, 2018). As the China Earthquake Administration reported, this earthquake caused 25 deaths and 176 492 injured or affected (Lei et al, 2018;Wang et al, 2018b).…”

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confidence: 99%

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GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China

Zhang

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. A Mw=6.5 earthquake struck the Jiuzhaigou region of Sichuan Province, China, at 21:19 LT on Tuesday, 8 August 2017, and triggered a large number of landslides. For mitigating the damages of earthquake-triggered landslides to individuals and infrastructures of the earthquake-affected region, a comprehensive landslide susceptibility mapping was attempted with an integrated weighted index model by combining the frequency ratio and the analytical hierarchy process approaches under a GIS-based environment in the heavily earthquake-affected Zhangzha town of the Jiuzhaigou region. For this purpose, a total number of 842 earthquake-triggered landslides were visually interpreted and located from Sentinel-2A images acquired before and after the earthquake at first, and then the recognized landslides were randomly split into two groups to establish the earthquake-triggered landslide inventory, among which 80 % of the landslides were used for training the integrated model and the remaining 20 % for validation. Nine landslide controlling factors were considered including slope, aspect, elevation, lithology, distance from faults, distance from rivers, land use–land cover, normalized difference vegetation index and peak ground acceleration. The frequency ratio was utilized to evaluate the contribution of each landslide controlling factor to landslide occurrence, and the analytical hierarchy process was used to analyse the mutual relationship between landslide controlling factors. Finally, the landslide susceptibility map was produced by using weighted overlay analysis. Furthermore, an area under the curve approach was adopted to comprehensively evaluate the performance of the integrated weighted index model, including the degree of model fit and model predictive capability. The results demonstrated the reliability and feasibility of the integrated weighted index model in earthquake-triggered landslide susceptibility mapping at a regional scale. The generated map can help engineers and decision makers assess and mitigate hazards of the earthquake-triggered landslides to individuals and infrastructures of the earthquake-affected region.

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“…With the rapid development of space technology and electronic information technology, remote sensing has been increasingly used in disaster emergency responses and rescues, with its advantages of rapid, macro, all-weather, and nearly all-day monitoring [5], and has become one of the most economical and effective solutions for landslide extraction [6]. It is of great significance to get information on a landslide situation as soon as possible, make a reasonable rescue plan, determine the potential hazards for key investigation, reasonably arrange the affected residents, and avoid secondary disasters [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model

et al. 2020

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Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.

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Landslides and dam damage resulting from the Jiuzhaigou earthquake (8 August 2017), Sichuan, China

Cited by 49 publications

References 30 publications

GIS based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India

GIS based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India

GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China

Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model

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