Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China

Du, Guoliang; Zhang, Yongshuang; Iqbal, Javed; Yang, Zhihua; Yao, Xia

doi:10.1007/s11629-016-4126-9

Cited by 174 publications

(84 citation statements)

References 55 publications

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“…For slope aspect, the slope aspects in the south direction (south, southeast, and southwest) were more prone to landslides. The reason for this is that these direction slopes are exposed to more sunlight or affected by the orientation of discontinuities controlling the landslides, which is the same as the conclusion proposed by Du [58].…”

Section: Predisposing Factors Analysis Of Informationsupporting

confidence: 57%

“…The plan curvature is reclassified into three classes: Thirteen layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors for producing LSM. The selection of 13 predictors was based on the works of previous researchers, collection of data availability and the experience and knowledge about landslide activities in the study area [24,58]. The continuous predictors, such as altitude, slope gradient, slope aspect, distance to faults, distance to roads, distance to rivers, NDVI, TWI, plan curvature, and profile curvature, were classified according to natural break classes and the previous study, and the discrete predictors, including lithology, annual precipitation, and land type, were classified based on the existing classification.…”

Section: Data Collectionmentioning

confidence: 99%

“…With the increase of slope gradient, the landslides increase. The reason for this is that the slope gradient not only affects the stress distribution inside the slope masses but also affects weathering layer depth and slope surface runoff [58]. However, the landslides are mainly concentrated in the altitude range of 276-715, which is due to the fact that the catchment areas are mainly concentrated in this range.…”

Section: Predisposing Factors Analysis Of Informationmentioning

confidence: 99%

See 2 more Smart Citations

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Qin

Cao

et al. 2019

Entropy

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Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.Entropy 2019, 21, 372 2 of 24 involving land use management as an efficient approach to reduce property damage and economic loss in landslide-prone areas [1,[6][7][8][9]. The outcome maps would be useful for general planned development activities and disaster management in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.Landslide susceptibility maps can be obtained using both qualitative (inventory-based and knowledge-driven methods) or quantitative approaches (data-driven methods and physically based models) [4,[10][11][12][13][14][15][16][17].Landslide inventory-based techniques, as a prelude to all other methods, include the collection of past landslide data, construction of databases, and production of susceptibility maps based on those data [18]. Landslide inventory mapping can be carried out using a variety of methods that were updated and summarized by Corominas et al. [17].Knowledge-driven methods that estimate landslide potential from the practical experience and expertise of the researcher are used by geomorphologists to analyze...

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Section: Predisposing Factors Analysis Of Informationsupporting

confidence: 57%

Section: Data Collectionmentioning

confidence: 99%

Section: Predisposing Factors Analysis Of Informationmentioning

confidence: 99%

See 1 more Smart Citation

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Qin

Cao

et al. 2019

Entropy

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“…The exposed strata of this area were mainly comprised of the Nieru Formation (T 3 n) and the Lure Formation (J 2 lr), the lithologies of which both constituted sandstone, metamorphic fine rock, and quartz sandstone. Since the moderate and low susceptibility areas were located far away from the fault and the river, the lithology was stable, and the occurrence of landslides was rare [51].…”

Section: Evaluation Results Of the Slope Unitsmentioning

confidence: 99%

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Wang

et al. 2017

IJGI

106

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Abstract:The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.

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“…NDVI is a quantitative parameter of vegetation coverage and reflects ecological environmental quality. It can directly affect the degree of soil erosion and the modification of the slope surface (Du et al 2017). The formation of plant root complexes in the surface soil can help maintain slope stability by enhancing the shear strength of slope soil Huang et al 2017).…”

Section: Predisposing Factorsmentioning

confidence: 99%

Fractal-Based Spatial Distribution Analysis of Geological Hazards and Measurement of Spatial Association With Hazard-Related Predisposing Factors

Zhou

Wang

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Fractal model as an effective solution to complex nonlinear problems or phenomena has been widely used to describe such complicated phenomenon as geological hazards. Quantitative analysis of the spatial distribution characteristics of geological hazards and measuring its fractal relation on a national scale are significant for the geological hazards prevention or mitigation. In this contribution, firstly, three typical geological hazards, such as landslides, collapses and mudslides, were taken as research objects for fractal analysis, and a detailed hazard inventory including 109,008 landslides, 55,178 collapses, and 28,914 mudslides cases were compiled as data samples. Next, the fractal dimensions describing the spatial distribution characteristics of geological hazard densities were calculated by the invariant fractal model, and then the internal classification of five common predisposing factors (elevation, slope, aspect, NDVI, and precipitation) was applied, and the relative density of geological hazard was calculated by the ratio of "hazard ratio" and "grid ratio" on the basis of 1 km × 1 km grid cells. Finally, the variable fractal model was introduced for measuring the spatial association among three typical geological hazards and five common predisposing factors, and the obtained fractal dimensions were regarded as the quantitative measure of the effect of predisposing factors on geological hazards. The results shows that the fractal dimensions of spatial distribution of landslide, collapse and mudslide densities are 1.3042, 1.5185 and 1.5897, respectively. Moreover, the relative densities of geological hazards also follows the fractal features with hazard-related predisposing factors, the elevation factor has the greatest impact on the landslide, collapse, and mudslide hazard, while other predisposing factors have different effects on different types of geological hazards.

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Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China

Cited by 174 publications

References 55 publications

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Fractal-Based Spatial Distribution Analysis of Geological Hazards and Measurement of Spatial Association With Hazard-Related Predisposing Factors

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