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

Wang, Qianqian; Wang, Dongchuan; Huang, Yong; Wang, Zhiheng; Zhang, Lihui; Guo, Qiaozhen; Chen, Wei; Chen, Wengang; Sang, Mengqin

doi:10.3390/su71215839

Cited by 46 publications

(22 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It will extend the observation period and possibly include landslide locations with a combination of predisposing factors that have not yet been represented in our susceptibility model [42]. More importantly, the landslide activity in the study area is characterized by occasional very intensive years with a dramatic increase in landslide activity.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

et al. 2017

View full text Add to dashboard Cite

Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In this model, information values of predisposing factors were used to characterize the possibility of landslides occurrence. The information value I( , H) of each landslide predisposing factor ( = 1,2, … , ) can be expressed as follows [21,36,37]: …”

Section: Information Value Model (Ivm)mentioning

confidence: 99%

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

Chen

Deng³

et al. 2017

IJGI

View full text Add to dashboard Cite

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.

show abstract

“…Some of the quantitative methods are bivariate and some of them are multivariate. Typical examples of quantitative methods are frequency ratio (FR) [14,15], information value model [16], weight of evidence (WoE) [17] and logistic regression (LR). In addition, some machine learning methods, such as support vector machines (SVM) [18,19], artificial neural network (ANN) [20] and backpropagation artificial neural networks (BPANN), have become increasingly popular in recent years.…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping

et al. 2016

View full text Add to dashboard Cite

Abstract:The development of landslide susceptibility maps is of great importance due to rapid urbanization. The purpose of this study is to present a method to integrate the subjective weight with objective weight for regional landslide susceptibility mapping on the geographical information system (GIS) platform. The analytical hierarchy process (AHP), which is subjective, was employed to weight predictive factors' contribution to landslide occurrence. The frequency ratio (FR) method, which is objective, was used to derive subclasses' frequency ratio with respect to landslides that indicate the relative importance of a subclass within each predictive factor. A case study was carried out at Tsushima Island, Japan, using a historical inventory of 534 landslides and seven predictive factors: elevation, slope, aspect, terrain roughness index (TRI), lithology, land cover and mean annual precipitation (MAP). The landslide susceptibility index (LSI) was calculated using the weighted linear combination of factors' weights and subclasses' weights. The study area was classified into five susceptibility zones according to the LSI. In addition, the produced susceptibility map was compared with maps generated using the conventional FR and AHP method and validated using the relative landslide index (RLI). The validation result showed that the proposed method performed better than the conventional application of the FR method and AHP method. The obtained landslide susceptibility maps could serve as a scientific basis for urban planning and landslide hazard management.

show abstract

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

Cited by 46 publications

References 40 publications

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

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

GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping

Contact Info

Product

Resources

About