Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China

Chen, Wei; Li, Wenping; Hou, Enke; Zhao, Zhou; Deng, Nian-dong; Bai, Hanying; Wang, Danzhi

doi:10.1007/s12517-014-1369-z

Cited by 91 publications

(34 citation statements)

References 51 publications

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“…A certain parameter may be an important controlling factor for landslide occurrence in one area but not in another [20]. According to the analysis of previous data and geological environment research literature on the sensitive area of landslides, the critical predisposing factors of landslides include regional geological conditions (fault structure, slope, aspect, formation lithology, etc.…”

Section: Selection Of Predisposing Factorsmentioning

confidence: 99%

“…The information value (IV) for each subclass of the factors is calculated with the following equation [20,30],…”

Section: Information Value (Iv) Modelmentioning

confidence: 99%

“…Landslide susceptibility maps are often validated using AUC [32,33]. This method has been widely used as a measure of performance of a predictive rule [20,34,35]. With the implementation of ROC analysis we can assess the prediction accuracy of a model [36].…”

Section: Selection Of Optimal Combination Based On Roc Curve Testmentioning

confidence: 99%

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Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Wang

Huang

et al. 2015

Sustainability

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Landslides are usually initiated under complex geological conditions. It is of great significance to find out the optimal combination of predisposing factors and create an accurate landslide susceptibility map based on them. In this paper, the Information Value Model was modified to make the Modified Information Value (MIV) Model, and together with GIS (Geographical Information System) and AUC (Area Under Receiver Operating Characteristic Curve) test, 32 factor combinations were evaluated separately, and factor combination group with members Slope, Lithology, Drainage network, Annual precipitation, Faults, Road and Vegetation was selected as the optimal combination group with an accuracy of 95.0%. Based on this group, a landslide susceptibility zonation map was drawn, where the study area was reclassified into five classes, presenting an accurate description of different levels of landslide susceptibility, with 79.41% and 13.67% of the validating field survey landslides falling in the Very High and High zones, respectively, mainly distributed in the south and southeast of the catchment. It showed that MIV model can tackle the problem of "no data in subclass" well, generate the true information value and show real running trend, which performs well in showing the relationship between predisposing factors and landslide occurrence and can be used for preliminary landslide susceptibility assessment in the study area.

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Section: Selection Of Predisposing Factorsmentioning

confidence: 99%

“…The information value (IV) for each subclass of the factors is calculated with the following equation [20,30],…”

Section: Information Value (Iv) Modelmentioning

confidence: 99%

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Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Wang

Huang

et al. 2015

Sustainability

Self Cite

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“…The information value based method has been widely applied as a statistical data-driven method recommended by experts [17] to assess landslide susceptibility [18,19]. Xu et al [18] used GIS and the information value model to evaluate debris flow susceptibility.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al [18] used GIS and the information value model to evaluate debris flow susceptibility. Chen et al [19] made a landslide susceptibility map using the information value model in the Chencang District of Baoji, China. debris flows and so on.…”

Section: Introductionmentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

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Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

Gardezi¹,

Usmani

Chen

et al. 2022

Geological Journal

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Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China

Cited by 91 publications

References 51 publications

Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

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