Identification of landslide spatial distribution and susceptibility assessment in relation to topography in the Xi’an Region, Shaanxi Province, China

doi:10.1007/s11707-014-0474-3

Front. Earth Sci.

2015, Vol. 9

Issue (3) : 449-462 https://doi.org/10.1007/s11707-014-0474-3

RESEARCH ARTICLE

Identification of landslide spatial distribution and susceptibility assessment in relation to topography in the Xi’an Region, Shaanxi Province, China

Jianqi ZHUANG^1,²,Jianbing PENG^1,^2,^*(

),Javed IQBAL^3,⁴,Tieming LIU²,Na LIU²,Yazhe LI¹,Penghui MA¹

1. School of Geological Engineering and Surveying of Changan University, Key Laboratory of Western China Mineral Resources and Geological Engineering, Xi’an 710054, China
2. Institute of Geo-hazards Mitigation of Chang’an University, Xi’an 710054, China
3. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
4. Department of Earth Sciences, COMSATS Institute of Information Technology, Abbottabad, Pakistan

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Abstract

Landslides are among the most serious of geohazards in the Xi’an Region, Shaanxi, China, and are responsible for extensive human and property loss. In order to understand the distribution of landslides and assess their associated hazards in this region, we used a combination of frequency analysis, logistic analysis, and Geographic Information System (GIS) analysis, with consideration of the spatial distribution of landslides. Using the GIS approach, the five key factors of surface topography, including slope gradient, topographic wetness index (TWI), height difference, profile curvature and slope aspect, were considered. First, the distribution and frequency of landslides were considered in relation to all of the five factors in each of three sub-regions susceptible to landslides (Qin Mountain, Li Mountain, and Loess Tableland). Secondly, each factor’s influence was determined by a logistic regression method, and the relative importance of each of these independent variables was evaluated. Finally, a landslide susceptibility map was generated using GIS tools. Locations that had recorded landslides were used to validate the results of the landslide susceptibility map and the accuracy obtained was above 84%. The validation proved that there is sufficient agreement between the susceptibility map and existing records of landslide occurrences. The logistic regression model produced acceptable results (the areas under the Receiver Operating Characteristics (ROC) curve were 0.865, 0.841, and 0.924 in the Qin Mountain, Li Mountain and Loess Tableland). We are confident that the results of this study can be useful in preliminary planning for land use, particularly for construction work in high-risk areas.

Keywords landslide distribution susceptibility assessment logistic model ROC Xi’an

Corresponding Author(s): Jianbing PENG

Online First Date: 21 January 2015 Issue Date: 20 July 2015

Cite this article:

Jianqi ZHUANG,Jianbing PENG,Javed IQBAL, et al. Identification of landslide spatial distribution and susceptibility assessment in relation to topography in the Xi’an Region, Shaanxi Province, China[J]. Front. Earth Sci., 2015, 9(3): 449-462.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-014-0474-3
https://academic.hep.com.cn/fesci/EN/Y2015/V9/I3/449

Fig.1 Landform characteristics of the Xi’an Region (left) and landform cross-section (right); (a) A?A′ cross-section; (b) B?B′ cross-section.

Fig.2 Simplified geological map of the four sub-regions in Xi’an, China.

Fig.3 Flowchart of landslide susceptibility analysis

Tab.1 The landslide frequencies in the three sub-regions in Xi’an and their geological characteristics

Fig.4 Frequency distribution of landslides in different classes of slope gradients in the three sub-regions of Xi’an. LSA grid (%): percent of each grid affected by landslides; Grid (%): percentage of grids in the domain; FR: percent of total grids affected by landslides; (a) Qin Mountain; (b) Li Mountain; (c) Loess Tableland.

Fig.5 Frequency distribution of landslides in different slope aspect classes in the three sub-regions of Xi’an. LSA grid (%): percent of each grid affected by landslides; Grid (%): percent of grids in the domain; FR: grids affected by landslides as a percentage of all grids; (a) Qin Mountain; (b) Li Mountain; (c) Loess Tableland.

Fig.6 Frequency distribution of landslides in different TWI classes in the three sub-regions of Xi’an. LSA grid (%): percent of each grid affected by landslides; Grid (%): percent of grids in the domain; FR: grids affected by landslides as a percentage of all grids; (a) Qin Mountain; (b) Li Mountain; (c) Loess Tableland.

Fig.7 Frequency distribution of landslides in different profile curvature classes in the three sub-regions. LSA grid (%): percent of each grid affected by landslides; Grid (%): percent of grids in the domain; FR: grids affected by landslides as a percentage of all grids; (a) Qin Mountain; (b) Li Mountain; (c) Loess Tableland.

Fig.8 Frequency distribution of landslides in different height difference classes in the three sub-regions of Xi’an. LSA grid (%): percent of each grid affected by landslides; Grid (%): percent of grids in the domain; FR: grids affected by landslides as a percentage of all grids; (a) Qin Mountain; (b) Li Mountain; (c) Loess Tableland.

Tab.2 Correlation between the percentage of actual landslides and the predicted probability of landslides

Fig.9 ROC curve. Results are described in the text. (a) Qin Mountain; (b) Li Mountain; (c) Loess Tableland.

Tab.3 Landslide distributions in different susceptibility classes

Fig.10 Susceptibility map generated from the logistic regression model for the three sub-regions in Xi’an, China. (a) is the Qin Mountain; (b) is the Li Mountain; (c) is the Loess Tableland.

Fig.11 Susceptibility map converted to a KML shape overlaid on Google Earth.

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