Landslide susceptibility assessment using evidential ...

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Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China Zhuo Chen, Shouyun Liang, Yutian Ke, Zhikun Yang & Hongliang Zhao To cite this article: Zhuo Chen, Shouyun Liang, Yutian Ke, Zhikun Yang & Hongliang Zhao (2017): Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China, Geocarto International, DOI: 10.1080/10106049.2017.1404143 To link to this article: https://doi.org/10.1080/10106049.2017.1404143

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Date: 28 November 2017, At: 05:09

Geocarto International, 2017 https://doi.org/10.1080/10106049.2017.1404143

Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China Zhuo Chena,b, Shouyun Lianga,b, Yutian Kea,b , Zhikun Yanga,b and Hongliang Zhaoa,b a

Downloaded by [Zhuo Chen] at 05:09 28 November 2017

Department of Geological Engineering, Key Laboratory of Mechanics on Disaster and Environment in Western China, Ministry of Education, Lanzhou University, Lanzhou, China; bSchool of Civil Engineering and Mechanics, Lanzhou University, Lanzhou, China

ABSTRACT

This study evaluates and compares landslide susceptibility maps of the Baxie River basin, Gansu Province, China, using three models: evidential belief function (EBF), certainty factor (CF) and frequency ratio (FR). First, a landslide inventory map is constructed from satellite image interpretation and extensive field data. Second, the study area is partitioned into 17,142 slope units, and modelled using nine landslide influence parameters: elevation, slope angle, slope aspect, relief amplitude, cutting depth, gully density, lithology, normalized difference vegetation index and distance to roads. Finally, landslide susceptibility maps are presented based on EBF, CF and FR models and validated using area under curve (AUC) analysis. The success rates of the EBF, CF and FR models are 0.8038, 0.7924 and 0.8088, respectively, while the prediction rates of the three models are 0.8056, 0.7922 and 0.7989, respectively. The result of this study can be reliably used in land use management and planning.

ARTICLE HISTORY

Received 3 July 2017 Accepted 25 October 2017 KEYWORDS

Landslide susceptibility; evidential belief function; certainty factor; frequency ratio; slope unit

1. Introduction Loess is distributed over approximately 631,000 km2 of China, which constitutes 6.6% of the total land area of the country (Liu 1985). The largest area of loess, known as the Loess Plateau, is situated in the central Yellow River valley, and is 100–300 m thick (Liu 1985). The loess is characteristically loose, contains large pores, has weak erosion resistance and well-developed vertical jointing (Wen and Yan 2014), which makes it prone to slope failure. As a result, the Loess Plateau has been subjected to many of the most serious landslides in China. In terms of spatiotemporal distribution, the landslides in this area show both concentration and decentralization, which, in combination with rapid economic development, increased human activity in the area and the impact of rainfall, results in relatively frequent landslides. To ensure safety of lives and property, and to distinguish regional and macroscopic landslide disaster prevention, it is necessary to distinguish areas most prone and least prone to landslides. This requires evaluation of historical landslides from the region, which can be achieved efficiently with geographic information systems (GIS) technology to produce spatial data of landslide susceptibility and a scientific

CONTACT Shouyun Liang

[email protected]

© 2017 Informa UK Limited, trading as Taylor & Francis Group


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basis for effective prevention of landslide disasters (Lee and Pradhan 2007; Mohammady et al. 2012; Demir et al. 2013). Landslide susceptibility reflects the spatial variation of the landslide and can answer the question of where a landslide is likely to occur (Ilia and Tsangaratos 2016). Traditional landslide susceptibility evaluations tend not to account for dynamic predisposing factors or time probability. They are based on existing landslide data, combined with geological and geomorphological conditions, and consider the probability of a landslide occurrence under established environmental conditions (Devkota et al. 2013). Based on a GIS platform, Guzzetti et al. (1999) used discriminant analysis and statistical model to map landslides and summarized the efficiency and limitations of each model Refice and Capolongo (2002) combined a simplified Newmark model and GIS system and completed a landslide susceptibility map. Guzzetti et al. (2005) obtained landslide data from a multi-temporal inventory map through the interpretation of aerial photographs taken along a time period. Along with the development of engineering geotechnical mechanics, artificial intelligence, non-linear science, data analysis and processing technology, more data mining models are applied and tend to be integrated with GIS software to carry out landslide susceptibility mapping. The most commonly used landslide susceptibility evaluation models are (Regmi et al. 2014): heuristic, deterministic and statistical. The heuristic model is an indirect, mostly qualitative mapping method that relies primarily on prior knowledge of the investigation in terms of all causes and instability factors in the study area, and takes into account geomorphological processes acting on the terrain. The model can take into account a large number of environmental factors, is time-efficient, and can be used for any scale of study (Guzzetti et al. 1999; Ayalew et al. 2004; Ruff and Czurda 2008). However, the heuristic model also has some drawbacks: it is strongly subjective, there are differences in the rules of judgement, and when new data becomes available, it is difficult to update the analytical results (Pellicani et al. 2014; Roodposhti et al. 2014; Zhang, Cai et al. 2016). The deterministic model is based on the physical and mechanical mechanisms of landslide deformation and failure, and usually uses a static model to calculate the safety factor of the landslide by considering the limit of equilibrium of a potential slip surface (Gökceoglu and Aksoy 1996; Cervi et al. 2010; Jia et al. 2012; Pradhan and Kim 2016a). The deterministic model is reliable and provides good depth of study of the landslide mechanisms. However, it is difficult to obtain model parameters in a wide range of susceptibility evaluations, and the model is difficult to use for complex landslides (Dai and Lee 2002; Gomez and Kavzoglu 2005; Raja et al. 2017). The statistical model is used to analyze the functional relationships between the landslide and their conditioning factors using appropriate mathematical models under the premise of extensive collection and processing of basic data to complete the landslide susceptibility classification (Baeza and Corominas 2001; Komac 2006; Althuwaynee et al. 2014a). The mathematical basis of the statistical model is rigorous but straightforward, and can quantify the statistical analysis error. Data storage management in this model is also convenient, and the results are more objective and accurate than other models (Fell et al. 2008; Yalcin et al. 2011). However, the model needs a large amount of data to determine the weighting, and the selection and classification of conditioning factors lacks some theoretical basis. Comparatively, the statistical model is favoured by researchers. A variety of statistical models have been applied to landslide susceptibility assessments, including logistic regression models (Ayalew and Yamagishi 2005; Van et al. 2006; Bai et al. 2010; Ozdemir and Altural 2013; Umar et al. 2014; Romer and Ferentinou 2016), weight of evidence models (Bettina and Birgit 2007; Kayastha et al. 2012; Ilia and Tsangaratos 2016), frequency ratio models (FR) (Lee and Pradhan 2007; Yilmaz 2009; Mohammady et al. 2012; Shahabi et al. 2014; Hong et al. 2016) and certainty factor (CF) models (Binaghi et al. 1998; Kanungo et al. 2011). Apart from the aforementioned methods, there are some data mining methods used in landslide susceptibility mapping, such as artificial neural networks (Ermini et al. 2005; Melchiorre et al. 2008; Conforti et al. 2014; Gorsevski et al. 2016; Pham et al. 2017), decision trees (Saito et al. 2009; Yeon et al. 2010; Althuwaynee et al. 2014b; Tsangaratos and Ilia 2016), support vector machines (Yao et al. 2008; Xu et al. 2012; Pradhan 2013; Kavzoglu et al. 2014; Peng et al. 2014) and evidential belief


GEOCARTO INTERNATIONAL

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functions (EBF) (Althuwaynee et al. 2012; Bui et al. 2012; Lee et al. 2013; Hong et al. 2016; Pradhan and Kim 2016b). The EBF model is a bivariate statistical method based on a generalized Bayesian theory of subjective probability, first proposed by Dempster (1967) and further developed by Shafer (1976). The EBF model was first applied to mapping of mineral potential (Moon 1989) and subsequently applied widely to evaluate groundwater potential (Nampak et al. 2014; Park et al. 2014). In recent years, the EBF model is used for landslide susceptibility mapping in which GIS support for the model was found to be easily implemented for landslide susceptibility zoning and for providing robust predictive capability (Ghosh and Carranza 2010; Althuwaynee et al. 2012; Lee et al. 2013; Pradhan and Kim 2016b). The CF model assumes that potential landslides will occur under conditions similar to that of past landslides, and is therefore based on the statistical relationships between past landslides and their influencing factors. Such models can be used to determine areas prone to slope failure and have been widely applied to landslide susceptibility mapping (Lan et al. 2004; Su et al. 2010; Devkota et al. 2013). The FR model is a relatively simple probabilistic model which provides straightforward results. The prediction of landslide susceptibility in the FR model is based on the joint conditional probability between the landslide and its influencing factors. Pradhan (2010), Choi et al. (2012), Demir et al. (2013), Shahabi et al. (2014) and Li et al. (2016) all successfully applied the FR model to landslide susceptibility mapping with good results. An important challenge in landslide susceptibility assessment is selection of the appropriate mapping unit. Grid cells are preferred by many scholars because computer processing and manipulation of the grid cells is more convenient (Anbalagan 1992; Van Westen 1994; Ayalew et al. 2004; Mathew et al. 2009). However, the disadvantages of the grid cells are that the integrity of the natural slope is fragmented and there is a lack of contact between geological and geomorphological information (Giles and Franklin 1998; MacMillan et al. 2004; Drăguţ and Eisank 2011). The slope unit is defined by the boundaries between geological and geomorphological information which reflects the interaction between each influencing factor, and has a clear geological and geomorphological significance. Such a slope unit is highly suited for the study of landslide susceptibility (Carrara et al. 1991; Giles and Franklin 1998; Guzzetti et al. 1999, 2005, 2006; Jia et al. 2012). The main difference between the work presented in this paper and previous studies is the integration of the slope unit into the EBF, FR and CF models for landslide susceptibility assessment. The accuracy of the three models is evaluated by calculating and comparing the area under the curve (AUC) with the landslide susceptibility maps, produced for the first time in the Baxie River basin. The results provide a quantitative framework for landslide prevention and mitigation in the region.

2. Study area The Baxie River basin is located in Dongxiang County, Linxia, Gansu Province, China (35°27–35°40′N and 103°15–103°45′E) and covers an area of 432 km2 (Figure 1). It is associated with the Tertiary Linxia basin of the Longxi Loess Plateau which lacks folds and fractures and contains mainly gently dipping to flat-lying strata. The major geomorphological units consist of valley plains and hilly areas of loess ridges, loess hills and ravines. The altitude is in the 1851–2465 m range, with slope angles of 0–43°. The study area has a continental, semi-arid climate, with an average annual rainfall of 540.6 mm (1981–2010). The highest rainfall takes place in monsoon (June to September) during which 58.5% of the total annual rainfall occurs. The highest and lowest recorded temperatures in the area are 28.8 °C and −23 °C, respectively. The average annual temperature is 5.6 °C. Landslides in the area are widely distributed and have occurred frequently (Figure 2). On 7 March 1983, a long-runout landslide with a volume of 3.1 × 107 m3 occurred on Saleshan Mountain, located on the northern side of the Baxie River basin. The landslide destroyed three villages, claiming 237 lives (Huang et al. 2016). In 1985, another significant landslide 600 m wide occurred, and also claimed many lives. More recently, on 10 July 2016, a voluminous landslide (5.0 × 105 m3) resulted from continuous rainfall and manual slope cutting. The landslide destroyed roads and buried some vehicles.


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Figure 1. Location of the study area and landslide inventory in the Baxie River basin, China.

Figure 2. Photographs of two landslides in the study area: (a) Slide within loess; (b) Bedrock contact landslide.

In the study area, a total of 249 landslides have been identified from the literature, remote sensing interpretations and field investigations. The landslides range in area from 3.52 × 103 m2 to 9.8 × 105 m2. Moreover, engineering activity such as road construction is common in this area, which also increases the slope instability.

3. Materials and methods 3.1. Landslide inventory map A landslide inventory map is used to quantitatively analyze the relationships between landslides and conditioning factors, and is also the basis of landslide susceptibility mapping. The reliability and precision of the collected data influences the accuracy of the resulting susceptibility map. In this paper a series of spatial data was collected, including Google Earth© images, 30 m-resolution ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) and remote sensing data from Landsat8 (https://earthexplorer.usgs.gov/). Geological map (1:200,000 scale) and road map data was also included. Detailed remote sensing interpretation and field investigations were conducted in the study area to develop a reliable and accurate landslide inventory map. As described above, 249 landslides were identified, 70% of which were randomly selected as a training


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data-set (174 landslides), and the remaining 30% (75 landslides) were used for model validation. All data layers were transformed into 30 × 30 m resolution grid layers, with the study area being classified into a total of 477,250 grids.


3.2. Division of slope units Reasonable classification of slope units can improve the reliability of landslide susceptibility mapping, which is traditionally done by extracting ridge lines and valley lines from high-resolution DTM (digital terrain models) to divide watersheds into separate slopes based on hydrological analysis (Giles and Franklin 1998; Guzzetti et al. 1999). However, Earth’s surface is a combination of horizontal and inclined surfaces, which cannot be displayed only by slope units. As a result, extracting slope units from a hydrological analysis model may result in a large number of unreasonably long strip units and slivers of polygons in existing flat terrains (Figure 3(a)). Moreover, when extracting river network data, the flow accumulation threshold strongly influences the size of slope units and needs to be manually processed. In general, the size of all slope units should not exceed the size of existing landslide units, otherwise unfavourable conditions may result, such as several landslides within a single slope unit. This would generate a poor evaluation with low reliability. Romstad and Etzelmüller (2009, 2012) used mean slope curvature to seek the mutations of slope angle and slope aspect boundaries in concave and convex watersheds, and were able to produce divisions of terrain units. This unit can distinguish horizontal and inclined surfaces, as well as eliminating unreasonable surface elements during the extraction process, thus reducing complex manual processing. However, this method moved depressions of those with relatively small absolute values of curvature, resulting in the inability to separate concave and convex surface elements in flat terrains. Yan (2016) updated this method by taking such depressions into consideration and was able to extract slope units (Figure 3(b)). A brief outline of the method can be generalized in the following five steps: (1) smoothing of the original DEM; (2) calculation of mean curvature (MEC); (3) extraction of flow direction; (4) identification of depressions in both the MEC image and the inverted MEC image; (5) using a watershed algorithm to both MEC images. Our work adopts this method, with the study area being divided into 17,142 slope units. The largest single unit is 265,061 m2, the smallest unit is 3120 m2 and the mean size is 39,763 m2 (Figure 4).

Figure 3. Results of different slope unit division methods: (a) hydrological analysis model where blue lines represent the concave watershed boundaries extracted from DEM and red lines represent convex watershed boundaries extracted from inverted DEM. (b) watershed segmentation of curvature where blue lines represent concave watershed boundaries extracted from curvature and red lines represent convex watershed boundaries extracted from inverted curvature (Yan 2016).


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Figure 4. Total area of derived slope units (17,142 units) in the study area.

3.3. Conditioning factors Based on the geological environment and landslide distribution pattern, a total of nine landslide conditioning factors were considered during the landslide susceptibility mapping: altitude, slope angle, slope aspect, relief amplitude, cutting depth, gully density, lithology, normalized difference vegetation index (NDVI) and distance to roads. From formerly classified slope units, the above conditioning factors were extracted using zonal statistics tools in ESRI ArcGIS® 10.2. Altitude is considered a vital factor that influences the occurrence and distribution of landslides, degree of weathering and human activities (Hong et al. 2016). In the study area, the elevation values are divided into four classes: 2200 m (Figure 5(a)). Slope angle is widely used in landslide susceptibility evaluation and is also an important factor when evaluating slope stability. The possibility of landslide occurrence increases with increasing slope angle (Demir et al. 2013). Slope angles in the study area are divided into six classes: 30° (Figure 5(b)). Slope aspect refers to variation in the intensity of sunlight received, which affects soil moisture evaporation and erosion. These factors influence the development of landslides (Ilia and Tsangaratos 2016). In the study area, slope aspect is divided into nine classes: flat (−1°), north (337.5–360°, 0–22.5°), north-east (22.5–67.5°), east (67.5–112.5°), south-east (112.5–157.5°), south (157.5–202.5°), south-west (202.5–247.5°), west (247.6–292.5°) and north-west (292.5–337.5°) (Figure 5(c)). Relief amplitude is the altitude difference between the highest and the lowest point in an area, and is used to reflect geomorphological surface variations that are closely related to the occurrence of landslides (Raja et al. 2017). In the study area, relief amplitude is divided into five classes: 250 m (Figure 5(d)). Cutting depth is the altitude difference between average elevation and the lowest elevation in a specified range of neighbouring areas around a single point (Jakimavičius and Mačerinskiene 2006). Cutting depth is used to reflect degrees of erosion and incision of a surface and in the study area is divided into four classes: 100 m (Figure 5(e)). Gully density is the total length of channels in a unit area. Increased gully density is indicative of intensive erosion, which raises the chance of landslide occurrences (Zhou et al. 2002). In the study area, gully density can be classified into six classes: 2.5 km/km2 (Figure 5(f)).

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Figure 5. Landslide conditioning factor maps: (a) elevation. (b) slope angle. (c) slope aspect. (d) relief amplitude. (e) cutting depth. (f) gully density. (g) lithology. (h) normalized difference vegetation index (NDVI). (i) distance to roads.

Lithology is an important factor in the formation and evolution of landslides, as it forms the material base for landslide generation (Yalcin et al. 2011). Slopes formed by different rock and soil masses have different physicomechanical properties. For example, loose rock and soil that are susceptible to weathering are typical of landslides (Pellicani et al. 2014). There are seven lithological strata in the study area (Figure 5(g) and Table 1). Vegetation can improve slope stability by reducing surface run-off and reinforcing the soil mass (Ding et al. 2017). In areas with scarce vegetation and limited coverage, soil and water loss as well as weathering erosion are dominated, which results in increased possibility of landslide occurrence. NDVI can be used to describe the degree of vegetation cover, which is positively correlated with the NDVI value (Choi et al. 2012). The NDVI value is determined by the equation NDVI = (IR−R)/(IR + R), where IR is the near-infrared band and R is the infrared band. NDVI in the study area is divided into six classes: −0.02–0.15, 0.15–0.2, 0.2–0.25, 0.25–0.3, 0.3–0.35 and > 0.35 (Figure 5(h)).


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Figure 5. (Continued)

Last, distance to roads is considered. Landslides commonly occur along or near roadways, often as a result of road construction that reduces slope stability (Kavzoglu et al. 2014). The distance to roads is divided into five classes (buffer zones) in 300 m intervals: 1200 m (Figure 5(i)). 3.4. Methodology 3.4.1. Evidential belief function model The EBF model consists of degrees of belief (Bel), disbelief (Dis), uncertainty (Unc) and plausibility (Pls), each in the 0–1 range (Ghosh and Carranza 2010). Bel and Pls represent the generalized Bayesian lower and upper limits of probability, respectively. Hence, Pls is greater than or equal to Bel. Unc is the difference between Pls and Bel and indicates the ignorance (or doubt) of the outcome. If

Table 1. Lithologies and associated ages of geology in the study area. No. 1 2 3 4 5 6 7

Code Q4 Q3 Q3 Q1–2 N2 l4 N2 l3 K1hk2

lithology Sandy clay, sand gravel Loam, sand gravel Loess Loam, gravel Mudstone Mudstone Sandstone, mudstone

Geological age Quaternary Quaternary Quaternary Quaternary Neogene Neogene Cretaceous


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Unc = 0, then Pls = Bel. Dis is the belief of the proposition being untrue based on given evidence and Dis = 1˗Unc˗Bel (Nampak et al. 2014). Suppose that in a study area, N(L) is the total number of landslide pixels and N(S) is the total number of pixels. Suppose further that Cij(j = 1,2,…,m) is the j-th class attribute of the landslide conditioning factors Ci(i = 1,2,…,n) and N(Cij) is the number of pixels in class Cij. Then, if N(Cij∩L) is the number of landslide pixels in class Cij, EBF can be obtained from (Ghosh and Carranza 2010):

Wcij L Belcij = ∑m j=1 Wcij L

(1)

where N(Cij ∩L)

Wcij L =

N(Cij ) N(L)−N(Cij ∩L)

(2)


N(S)−N(Cij )

likewise,

Wcij L̄ Discij = ∑m ̄ j=1 Wcij L

(3)

N(Cij )−N(Cij ∩L)

Wcij L̄ =

N(Cij ) N(S)−N(L)−[N(Cij )−N(Cij ∩L)]

(4)

N(S)−N(Cij )

The conditional probability that a landslide exists in the absence of Cij is shown in Equation (2) and WcijL is the weight of Cij that supports the belief that a landslide exists rather than being absent. Equation (4) is the conditional probability that a landslide does not exist, given the presence of Cij. Thus, WcijL is the weight of Cij that supports the belief that a landslide is more absent than present. For a case of Cij with no landslide occurrence (Belcij=0), then Discij is reset to 0. 3.4.2. Certainty factor model The CF model is one of the proposed favourability functions to deal with combining different data layers and heterogeneous input data (Devkota et al. 2013). The value of CF, defined as a probability function, was first proposed by Shortliffe and Buchanan (1975) and later improved by Heckerman (1986), as follows:

⎧ PP − PP a s ⎪ , PPa ≥ PPs ⎪ PPa (1 − PPs ) CF = ⎨ PPa − PPs ⎪ , PPa 1, it indicates a higher correlation and if the ratio is 250 0–50 50–75 75–100 >100 0–0.5 0.5–1 1–1.5 1.5–2 2–2.5 >2.5 1 2 3 4 5 6 7 −0.02–0.15 0.15–0.2 0.2–0.25 0.25–0.3 0.3–0.35 >0.35 1200

Landslide occurrence 15 56 69 34 9 30 44 55 31 5 0 0 26 35 51 32 27 3 3 41 95 29 6 17 73 63 21 10 16 44 72 28 4 3 18 119 0 18 16 0 0 14 58 62 35 5 38 79 28 16 13

Pixels in domain 22240 44065 112662 298283 72854 143796 159693 74904 20422 5581 326 21652 74761 95809 122960 93158 54675 13909 77250 258807 111364 25260 4569 128477 221277 98162 29334 26206 109725 138590 129173 56724 16832 16901 40150 292184 3897 72786 51264 68 1695 7807 39409 92931 150075 185333 91819 128368 81406 56078 119579

Bel 0.218 0.526 0.240 0.016 0.027 0.043 0.060 0.220 0.430 0.221 0.000 0.000 0.156 0.165 0.197 0.153 0.234 0.096 0.008 0.023 0.341 0.309 0.319 0.054 0.154 0.405 0.387 0.170 0.055 0.134 0.308 0.230 0.104 0.104 0.274 0.299 0.000 0.140 0.183 0.000 0.000 0.384 0.405 0.167 0.040 0.003 0.219 0.421 0.174 0.142 0.045

Dis 0.250 0.250 0.250 0.250 0.167 0.167 0.167 0.167 0.167 0.167 0.000 0.000 0.167 0.167 0.167 0.167 0.167 0.167 0.200 0.200 0.200 0.200 0.200 0.250 0.250 0.250 0.250 0.167 0.167 0.167 0.167 0.167 0.167 0.200 0.200 0.200 0.000 0.200 0.200 0.000 0.000 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200

Unc 0.532 0.224 0.510 0.733 0.806 0.791 0.774 0.614 0.404 0.612 1.000 1.000 0.678 0.668 0.637 0.680 0.600 0.737 0.792 0.777 0.459 0.491 0.481 0.696 0.596 0.346 0.363 0.664 0.778 0.700 0.526 0.604 0.729 0.696 0.526 0.501 1.000 0.660 0.617 1.000 1.000 0.416 0.395 0.633 0.760 0.796 0.582 0.379 0.626 0.658 0.755

FR 1.850 3.486 1.680 0.313 0.339 0.572 0.756 2.014 4.164 2.457 0.000 0.000 0.954 1.002 1.138 0.942 1.355 0.592 0.107 0.435 2.340 3.149 3.602 0.363 0.905 1.760 1.964 1.047 0.400 0.871 1.529 1.354 0.652 0.487 1.230 1.117 0.000 0.678 0.856 0.000 0.000 4.919 4.037 1.830 0.640 0.074 1.135 1.688 0.943 0.783 0.298

CF 0.842 0.633 0.384 −0.836 0.200 −0.300 −0.228 0.197 0.635 0.618 −1.000 −1.000 −0.468 0.229 0.312 −0.226 0.020 −0.434 −0.087 −0.602 0.511 0.636 0.602 −0.394 −0.143 0.470 0.118 0.480 −0.012 −0.047 −0.028 0.020 −0.800 0.102 −0.126 0.207 −1.000 0.015 0.030 −1.000 −1.000 0.896 0.669 0.537 −0.498 −0.880 0.249 0.388 0.088 −0.586 −0.706


/ Mi M Fri = / Ni N

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(6)

where Mi is the number of pixels with landslides for each subclass conditioning factor, M is the total number of landslides in the study area, Ni is the number of pixels in the subclass area of each factor and N is the number of total pixels in the study area.

4. Results


4.1. Evidential belief function model The spatial relationship between a landslide and its conditioning factors using the EBF model is shown in Table 2. The Bel value is largest (0.526) when the altitude is in the 2000–2100 m range, followed by 2100–2200 m (0.240) and 2200 m. This indicates that a landslide is more likely to occur within the altitude range 20° and thus the probability of landslide occurrence increases. The maximum Bel value (0.430) appears in the 25–30° slope angle range, followed by > 30° (0.221) and 20–25° (0.220). The Bel value decreases when the slope angle is 150 m are significantly higher than below 150 m, indicating that 150 m relief amplitude may be critical for the development of landslides in the region. As relief amplitude increases, the Bel value increases, indirectly indicating that the larger the relief amplitude, the greater the probability of landslide occurrences. The Bel value is largest (0.405) when the cutting depth is in the 75–100 m range, followed by depths > 100 m (0.387), indicating that landslides are more likely to occur within the cutting depth range > 75 m. As the cutting depth decreases, the probability of landslide occurrences decreases. Landslides are common at a gully density of > 1.5 km/km2, and the maximum Bel value (0.308) is reached at a gully density of 1.5–2 km/km2, followed by a value of 0.230 at 2–2.5 km/km2. Landslides are more likely when gully density is high because the higher soil moisture and pore water pressure decreases slope stability. The Bel value is largest (0.299) in Class 3 lithologies, followed by Class 2 (0.274), Class 6 (0.183) and Class 5 (0.140). The minimum Bel value appears in Classes 4 and 7. Class 3, 5 and 6 lithologies cover a large area, with characteristic loose structures and well-developed joints, thus these three lithologies are most prone to landslides. Conversely, other lithology classes cover less and more gentle terrain, resulting in less susceptibility to landslides. The largest Bel value (0.405) occurs when NDVI is in the 0.2–0.25 range, followed by 0.15–0.2 (0.384) and 0.25–0.3 (0.167). As NDVI increases, the probability of landslide occurrence gradually decreases. In terms of distance to road, Bel values of 0.219 and 0.421 were found between distances of 0–300 m and 300–600 m, respectively. It should be noted that roads in this densely populated region are always


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Figure 6. Landslide susceptibility map produced by the EBF model.

constructed along rivers, with associated frequent engineering activities, resulting in most landslides developing near roads. The landslide susceptibility index of the EBF model was calculated using ArcGIS® 10.2 to calculate, and according to the Jenks natural breaks method, the degree of vulnerability of the area is divided into five categories: very low, low, moderate, high and very high (Figure 6). These categories correspondingly cover 20, 26, 13, 25 and 16% of the total study area. The very high category contains 52% of the total landslides, while the high area contains 30% the total landslides. The moderate, low and very low categories contain 9, 7 and 2% of the total landslides, respectively. 4.2. Certainty factor model Table 2 shows the spatial relationship between the landslide and its conditioning factors obtained by the CF model. The values of CF are highest at altitudes 30° (0.618) and 20–25° (0.197). Overall, higher CF values result in increased probability of landslide occurrences. CF values are lowest at slope angles 150 m. Landslides are more likely to occur, and the maximum CF value is reached (0.636), in the 200–250 m range, followed by amplitudes > 250 m (0.602). The CF value below 150 m is lower, and thus the probability of landslides occurring decreases. The lowest CF values (−0.602) occur in the 100–150 m range. In terms of cutting depth, the CF value is highest (0.470) at a depth of 75–100 m, while the lowest values (−0.394) occur in the 0–50 m range. These results indicate that landslides are more likely to occur in areas of significant cutting depths. The maximum CF value (0.480) occurs at a gully density of 0–0.5 km/km2 and the minimum value (−0.800) occurs in the 1.5–2 km/km2 range. Compared


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with the gully density, the other conditioning factors such as altitude, slope angle, slope aspect, relief amplitude and cutting depth appear to play a more important role in landslide occurrences. The highest CF value (0.207) occurs in Class 3 lithologies, followed by Class 1 (0.102), Class 6 (0.030) and Class 5 (0.015). The minimum value of CF appears in Class 4 and 7 lithologies, indicating that landslides are less likely to occur in these slopes. The largest CF value (0.896) occurs in the NDVI range of 0.15–0.2, followed by 0.2–0.25 (0.669) and 0.25–0.3 (0.537). The CF value is lowest (−0.880) above 0.35, and the probability of landslide occurrences is low. When the distance to roads is in the 300 m range, the CF value is 0.249, which increases to 0.388 when the distance increases to 300–600 m. With increasing distance to the road, the probability of landslide occurrences decreases. The CF model is used to obtain the landslide susceptibility map, which is divided into five susceptibility grades using natural breaks method: very low, low, moderate, high and very high (Figure 7). These grades cover 20, 19, 20, 22 and 19% of the total area of the region, respectively. Very high- and high-grade areas contain 51 and 28% of all landslides, respectively. Moderate grade areas contain 12% of landslides, while low- and very low-grade areas contain 7 and 2% of the landslides, respectively. 4.3. Frequency ratio model Table 2 shows the spatial relationship between landslide and the conditioning factors obtained from the FR model. The values of FR are > 1 when altitude is 1 when the slope angle is > 20° (high probability of landslides) reach maximum values (4.164) at slope angles of 25–30°. Slope angles