Physical vulnerability assessment of buildings exposed to landslides in India

Singh, Aditi; Kanungo, Debi Prasanna; Pal, Shilpa

doi:10.1007/s11069-018-03568-y

Cited by 40 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…traditional methods make a comprehensive analysis of various influencing factors of landslides (Amashi et al, 2019;Baena et al, 2019;Ciurleo et al, 2019;Hu et al, 2019;Rao et al, 2017;Singh et al, 2019;Xie et al, 2015), ignoring the landslides distribution mechanism itself, while this paper focuses on the analysis of the landslides distribution state itself in tributary, so this method can be generally applied to identify landslide sources in more general cases. For example, the Wenchuan earthquake has about 11,000 individual landslide points (Gorum et al, 2011), and it is found that these landslides are distributed mainly in relatively high EI tributaries (Tian et al, 2019;Xiang et al, 2015).…”

Section: General Application Of Ei For Landslide Source Identificationmentioning

confidence: 99%

“…The comprehensive analysis of various factors is equivalent to the black box theory, landslide susceptibility assessment over large areas is considered a preliminary step for the planning or design of the most appropriate risk mitigation measures. The use of statistics and physics based models is considered a useful tool for landslide susceptibility assessment (Amashi et al, 2019;Baena et al, 2019;Ciurleo et al, 2019;Hu et al, 2019;Rao et al, 2017;Singh et al, 2019;Xie et al, 2015). However, the research of these methods was mainly focused on the gully scale.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Landslides distribution at tributaries with different evolution stages in Jiangjia Gully, southwestern China

Tian¹,

Li²,

Tian³

2020

Preprint

View full text Add to dashboard Cite

Abstract. Landslide susceptibility assessment is of great significance for the disaster prediction and prevention. At present, most studies used statistical methods by the influence factors of landslide distribution, or based on physical models to determine the assessment result, the research of these methods was mainly focused on the gully scale. At the same time, these methods did not focus on the specific principle of material storage. In this paper, the surface erosion index, being the integral of the hypsometric curve, is adopted to explore the landslides distribution characteristic in different tributaries of the gully. Firstly, 81 tributaries of JJG are taken from DEM with 10 m grid cells, and the hypsometric curves are used to characterize their evolution stages; five stages are identified by the evolution index (EI, the integral of the hypsometric curves) and most tributaries are in relative youth stage with EI between 0.5 and 0.6. Then 906 landslides are interpreted from Quickbird satellite image of 0.61 m resolution. It is found that LD (LD = landslides number in a tributary/ the tributary area) increases exponentially with EI, while LAp (LAp = landslides area in a tributary/the tributary area) fluctuates with EI, meaning that landslides are inclined to occur in tributaries with EI between 0.5 and 0.6, and thus these tributaries are the main material sources supplying for debris flows.

show abstract

Section: General Application Of Ei For Landslide Source Identificationmentioning

confidence: 99%

mentioning

confidence: 99%

Landslides distribution at tributaries with different evolution stages in Jiangjia Gully, southwestern China

Tian¹,

Li²,

Tian³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…At this level, potential losses and consequences (i.e., physical or economic vulnerability) can already be partially quanti ed, for example, by clustering assets with similar characteristics (Puissant et al 2014). Micro-scale level analyses are performed for local areas, quantifying physical, social, environmental and economic vulnerabilities and aiming to implement technical and protective measures (e.g., Singh et al 2019;Holcombe et al 2012). Therefore, the amount of data required to conduct such studies is proportional to the detail of the analysis scale (Puissant et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Multitemporal Landslide Exposure And Vulnerability Assessment In Medellín, Colombia

Kühnl¹,

Sapena

Wurm

et al. 2022

Preprint

View full text Add to dashboard Cite

Landslides are often deadly natural events. Steep slopes and certain loose soil types are predestined areas for them. Moreover, in the context of climate change, extreme weather events such as heavy rainfall, which often trigger landslides, are becoming even more likely. All this is well known. It therefore stands to reason that this knowledge will lead to the avoidance of these risks. On the other hand, however, there are highly dynamic urbanization processes which often overtake formal urban planning processes by rising population figures and areal expansion. In the course of these processes, economically-deprived population groups often have no other option than to informally build on high-risk areas. Against these backgrounds, we systematically examine in this study how these risks develop over a 24 years’ time period with respect to the city-wide exposure and in particular with respect to different social groups. For this purpose, we use heterogeneous input data from remote sensing, hazard maps and census data. Our case study is the city of Medellín in Colombia. We develop and apply a set of methods integrating the heterogenous data sets to map, quantify and monitor exposure and social vulnerability at a finer spatial resolution than administrative units. Our results document first of all the highly dynamic growth in total population and urban areas. However, our results reveal that the city's expansion is socially unevenly distributed. People of higher vulnerability proxied by informal settlements are found to settle in significantly more areas exposed to landslides. This study proposes a methodological set-up that allows to monitor exposure and social vulnerability over long time spans, allows to bring inequality into the spotlight and provide decision makers with better information to develop socially responsible policies.

show abstract

“…Urban planning has an essential role in minimizing risk. The higher level of vulnerability of buildings in cities is triggered by an increase in population and unclear regulations [6]. Built-up land has a considerable risk of earthquake disaster [7,8].…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of the urban physical vulnerability using remote sensing and geographic information systems (case study: Yogyakarta City)

Arif

Wardhana

Martiana

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Vulnerability mapping in urban planning is very important to reduce damage caused and loss of life due to disasters. This paper discussed the mapping of urban physical vulnerability using remote sensing, and geographic information systems (GIS) approaches. Physical vulnerability parameters used were building density, distribution of public facilities, slope, and land use. Landsat 8 OLI satellite imagery used remote sensing data to obtain building density information using the Normalised Difference Built Index (NDBI) and land use interpretation approaches. With the use of GIS, the spatial distribution of urban vulnerabilities could be known through the overlay of each indicator. The results of the analysis obtained that the physical vulnerability classes of Yogyakarta City were very high (26%), high (29%), moderate (29%), low (16%), and very low (0%). Some areas categorized as vulnerable to very vulnerable are areas with high building density, namely the Sub-districts of Danurejan, Kraton, Gedongtengen, Jetis, Ngampilan, Pakualaman, Mantrijeron, parts of Gondomanan and Tegalrejo.

show abstract

Physical vulnerability assessment of buildings exposed to landslides in India

Cited by 40 publications

References 43 publications

Landslides distribution at tributaries with different evolution stages in Jiangjia Gully, southwestern China

Landslides distribution at tributaries with different evolution stages in Jiangjia Gully, southwestern China

Multitemporal Landslide Exposure And Vulnerability Assessment In Medellín, Colombia

Spatial analysis of the urban physical vulnerability using remote sensing and geographic information systems (case study: Yogyakarta City)

Contact Info

Product

Resources

About