GIS-based landslide spatial modeling in Ganzhou ...

Arab J Geosci (2016) 9:112 DOI 10.1007/s12517-015-2094-y

ORIGINAL PAPER

GIS-based landslide spatial modeling in Ganzhou City, China Haoyuan Hong 1 & Seyed Amir Naghibi 2 & Hamid Reza Pourghasemi 3 & Biswajeet Pradhan 4

Received: 27 November 2014 / Accepted: 10 September 2015 # Saudi Society for Geosciences 2016

Abstract Landslide susceptibility mapping is among the first works for disaster management and land use planning activities in a mountain area like Ganzhou City. The aims of the current study are to assess GIS-based landslide spatial modeling using four models, namely data-driven evidential belief function (EBF), frequency ratio (FR), maximum entropy (Maxent), and logistic regression (LR), and to compare their performances. At first, a landslide inventory map was prepared according to aerial photographs, satellite images, and extensive field surveys. In total, 3971 landslide events were recognized in the study area that used 2979 landslides (75 %) for modeling and 992 landslide events (25 %) for validation. In the next step, the landslideconditioning factors, namely slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index (TWI), slope-length (LS), lithology, normalized difference vegetation index (NDVI), distance from rivers, distance from faults, distance from roads, and rainfall, were derived from the spatial database. Finally, landslide susceptibility maps of Ganzhou City were mapped in ArcGIS based on EBF, FR, Maxent, and LR

approaches and were validated using the receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that in the landslide susceptibility maps using the EBF, FR, Maxent, and LR models, the area under the curve (AUC) values were 0.7367, 0.7789, 0.7903, and 0.8237, respectively. Therefore, it can be concluded that all four models have AUC values of more than 0.70 and can be used in landslide susceptibility mapping in the study area. Also, the LR model had the best performance in the current study. Meanwhile, the mentioned models (EBF, FR, Maxent, and LR) showed almost similar results. The resultant susceptibility maps produced in the current study can be useful for land use planning and hazard mitigation purposes in the study area. Keywords Landslide susceptibility mapping . Evidential belief function . Frequency ratio . Logistic regression . Maximum entropy . Ganzhou City

Introduction * Hamid Reza Pourghasemi [email protected]; [email protected] 1

Jiangxi Provincial Meteorological Observatory, Jiangxi Meteorological Bureau, No. 109 ShengfuBeier Road, Nanchang 330046, China

2

Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Noor, Mazandaran, Iran

3

Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

4

Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia

Globally, landslides are among the most important geological hazards and are responsible for substantial economic and human losses (Del Ventisette et al. 2012). To reduce and manage landslide-related disasters, it is important to assess areas susceptible to landslides. Hence, in recent years, the assessment of landslide susceptibility modeling has become one of the foremost topics of researches worldwide (Bhandary et al. 2013). Landslide susceptibility describes the physical attributes of potential landslides in terms of dimensions and occurrences. Landslide susceptibility zonation is a very important content of landslide hazard forecast modeling (Bagherzadeh and Mansouri Daneshvar 2012). In hilly and mountainous areas, on-site methods of slope instability analysis are not always economically and practically suitable to carry out a systematic investigation of landslide phenomena at the regional scale, because of both huge extension of

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Arab J Geosci (2016) 9:112

Fig. 1 Landslide location map of the study area

observed areas and, frequently, difficult accessibility (Cigna et al. 2012). Therefore, various methods have been proposed to assess landslide susceptibility, with increasing use of geographic information system (GIS) in mountainous areas (Lee et al. 2012; Roering 2012; Huang and Fan 2013), for example, analytical hierarchy process (AHP) (Pourghasemi et al. 2012a; Hasekiogullari and Ercanoglu 2012; Demir et al. 2012), bivariate statistical analysis (Nandi and Shakoor 2009; Raman and Punia 2012), multivariate regression methods such as logistic regression (Devkota et al. 2013; Mărgărint et al. 2013; Pourghasemi et al. 2013), weights-of-evidence/Bayesian method (BonhamCarter 1991; Regmi et al. 2010; Pourghasemi et al. 2012a, b), data mining using fuzzy logic (Pradhan 2011; Akgun et al. 2012a, b; Pourghasemi et al. 2012c), artificial neural networks (ANN) (Song et al. 2012; Zare et al. 2012; Conforti et al. 2014), decision tree (Tien Bui et al. 2012a; Pradhan 2012), spatial multicriteria evaluation (Pourghasemi et al. 2012b), evidential belief functions (Althuwaynee et al. 2012; Tien Bui et al. 2012a, b; Pradhan et al. 2014), maximum entropy (Constantin et al. 2011; Pourghasemi et al. 2012d; Jaafari et al. 2014), support vector machine (SVM) (Ballabio and Sterlacchini 2012; Pourghasemi et al. 2013; Peng et al. 2014), and neuro-fuzzy (Tien Bui et al. 2011; Sdao et al. 2013). Also, frequency ratio and maximum entropy models were implemented in other fields including groundwater potential mapping (Davoodi Moghaddam et al. 2013; Naghibi et al. 2014; Pourghasemi and Beheshtirad 2014). Also, Karami et al. (2015) investigated the capability of the objectbased and pixel-based image classification methods for gully erosion mapping as a natural hazard.

Which is the best method is still debated and inconclusive, and some researchers have conducted comparative experiments using several methods and select the most appropriate (Mohammady et al. 2012; Feizizadeh and Blaschke 2013; Regmi et al. 2013; Zizioli et al. 2013). Among the various susceptibility mapping methods, logistic regression (LR) presents certain advantages for studying landslides of soil and/or weathered rocks (Bai et al. 2011, 2013; Shirzadi et al. 2012). Ganzhou City, located in the south part of Jiangxi Province, has been heavily affected by landslides in recent years. Landslides are normally triggered by heavy rainfall, but very few attempts have been made to forecast their location or prevent their damage in the Jiangxi Province. Previously, hardly any investigations of landslide susceptibility analysis have been carried out in Ganzhou City. Therefore, in this study, was tried to model landslide susceptibility in Ganzhou City. In literature, there are different bivariate and multivariate approaches for landslide susceptibility modeling. However, a comparison of these approaches is not commonly encountered. So, the main purpose of the present study is to compare the accuracy of landslide susceptibility maps produced by evidential belief function (EBF), frequency ratio (FR), maximum entropy (Maxent), and LR models to obtain more accurate and reliable estimates of landslide modeling and thus generate more useful landslide susceptibility maps for Ganzhou City of China. Also, using the mentioned models and a large proportion of the known landslide locations was tried to determine the relationships between landslides and conditioning factors in the study area.


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Study area Ganzhou City is located in the upper reaches of Ganjiang River, south of Jiangxi Province. The main part of the area is rather hilly and mountainous, covered either by coniferous forest or bare soil. The study site lies between latitudes 24° 29′ N to 27° 09′ N and longitudes 113° 54′ E to 116° 38′ E. It covers an area of about 39, 400 km2 (Fig. 1). Ganzhou City is rich in mineral resources and has one of the key national nonferrous metal bases, and since the demand for mineral resources is growing, to a certain extent, the city’s vegetation and land use has been affected. Also, Ganzhou City belongs to the subtropical hilly mountain humid monsoon climate, with an average annual precipitation of 1461.8 mm, distributed on 143 rainy days, and an average annual temperature of 21.9 °C, according to meteorological data of Ganzhou City (Ming-Rong et al. 2007). Rainfall peaks occur in the months of February-August, with extremely evoked geological disasters. The main triggering factor is the intense rainfall, but human action, mainly land cover changes and excavations, also plays an important role in increasing the sensitivity of the surface layer to the effects of precipitation. Furthermore, the other reasons for this high vulnerability can be related to the uncontrolled deforestation, overgrazing, and offensive tillage activities (Heshmati Fig. 2 Flowchart of the used methodology in the study area

et al. 2011). As a fact, Ganzhou City is known as one of the most landslide-prone areas in the region of Jiangxi Province, because of its geostructural, geomorphological, and climatic features and human activities (Wei et al. 2005, 2007).

Methodology Figure 2 shows the flowchart of the applied methodology in the study area. In general, the flowchart consists of three phases: (1) data integration and analysis (data used); (2) landslide susceptibility modeling using four models, namely EBF, FR, Maxent, and LR; and (3) validation of these models, their comparison, and the selection of the best model according to receiver operating characteristic (ROC) curves. Data integration and analysis Landslide inventory mapping Landslide inventory mapping is the systematic mapping of existing landslides in the study area using different techniques, such as field survey, aerial photograph, satellite images

Effective factors

Digital elevation model (DEM) Slope angle Slope aspect Altitude Plan curvature Profile curvature TWI LS

Distance to rivers Distance to faults Distance to roads

Dependent variables (Landslide locations)

Lithology NDVI Rainfall

Training landslides (70% or 2,979)

Validation landslides (30% or 992)

Application of EBF, FR, Maxent, and LR models

Landslide susceptibility maps

ROC curve and validation of models

Selection of the best model

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Table 1

Types of geological formation of the study area

No.

Unit name

Lithology

Geological age

A

Zishan Group, Yunshan Group, The Group Shed, Three Beach Group, Dong Group peaks Huanglong

Quartz conglomerate, conglomerate, and sandstone; coarse sandstone, siltstone; calcareous sandstone, sandstone, siltstone interbedded carbonaceous shale, and coal folder Thick-bedded limestone, dolomitic limestone when the clip, dolomite containing cherty nodules or strips Light gray, gray dolomite limestone biological; mapping light gray bio limestone, dolomitic limestone local folder Gray and purple, purple sandstone, siltstone, silty shale interbedded gray-green sandstone Gray calcareous mudstone, siltstone limestone; gray folder purple sand, siltstone, silty sandstone shale chamosite, oolitic Gray calcareous mudstone, siltstone limestone; gray folder purple sand, siltstone, silty shale, oolitic Kimberlite Purple mudstone with sandstone, siltstone Fish cents Khouang conglomerate Coarse gray feldspar quartz sandstone, pebbly sandstone, fine sandstone, sandy shale, siltstone, carbonaceous shale, and coal line Early Jurassic granite Adamellite

Carboniferous

Pot-day Group B

C

D

Group in the shed, XiaShan Group, Yunshan Group Ma Shan Group, Ocean Lakes Group Yang Jiayuan Group, Ma Shan Group, Ocean Lakes Group Path unit Jiang’s Group Xiahu Group Lin North Hills Water Group

The new super-unit springs Ancient walled unit overcast, Huang Pi super elements City River unit, a small cloth unit, per small unit Darjeeling Bay Group Gexian Hill super elements Yellow sand unit Iris Group, Yutian Group Nine Cents Soup Mufushan, under a long hill, Saiyang off super unit, Huang Xie, Xihuashan super elements Long Island super elements

E

Car-step unit West Mountain super elements Torches Hills Danxia Group

F

G

Liukeng Unit Yang Bridge Group Hongshan Group Tangxia super elements Wei Fang Group Longtou Group

H

Small river group Taoxi (rock) Group

I

Kamiyama Group

J

Xiakeng Group Guan Tian Chao Unit The Jewish ultra unit

Carboniferous Carboniferous Devonian Devonian Devonian Paleogene Paleogene Paleogene Jurassic

Jurassic Jurassic

Basaltic andesite clip, dacitic-andesitic tuff lava, andesitic tuffs, and volcanic breccia (Two long, K-feldspar) granite Fine-grained, medium-grained (porphyritic) biotite granite Dark gray, gray-green basalt or basaltic andesitic (Two long, K-feldspar) granite

Jurassic Jurassic Jurassic Jurassic Jurassic

Diorite

Jurassic

Gabbro (diabase) Medium-grained porphyritic biotite granite Purple siltstone, mudstone conglomerate, containing gypsum, thenardite Volcanic mass conglomerate, rhyolite, rhyolite-dacitic lava, tuff, basalt Granite porphyry Moraine clay conglomerate, magnetite quartzite, conglomerate, even siliceous rocks Leptynite, schist clip becomes conglomerate, marble Hornblende pyroxenite Gray, dark gray phyllitic mudstone, chert clip metamorphic quartz sandstone With charcoal gray and black rocks, central light gray fine-grained limestone, gray limestone with chert nodules, calcirudite Flint limestone, siliceous rocks to sand, shale carbonaceous shale, limestone convex mirror Biotite-plagioclase leptynite, two plagioclase leptynite folder sericite schist Gray-green feldspar quartz sandstone, silty phyllite, black slate; interbedded tuff and slate Granodiorite rocks of Proterozoic Diorite, quartz, monzonite Biotite, granite

Jurassic Jurassic Cretaceous Cretaceous Cretaceous South-Sinian South-Sinian Ordovician Ordovician Ordovician

Middle Triassic Permian Quaternary Quaternary Silurian Silurian


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Table 1 (continued) No.

Unit name

Lithology

Geological age

K

TieShiKou Group Anyuan Group

Triassic Triassic

L

Lechang Gorge Group

M

Bauhinia Small Group, Tea Group Head, Xiang Nan Group Eight Village Groups

Yellow-green siltstone, calcareous mudstone, local marl Sand, shale, chert conglomerate clip the bottom seam, tuffaceous sandstone, tuff, tuffaceous mudstone, oil shale, coal lines, and olivine basalt Purple gray feldspar, quartz, sandstone, silty slate; pale gray chert, phyllite Lithic sandstone, limestone; sandstone, sandy slate; lithic sandstone, carbonaceous slate Dark gray lithic sandstone, siltstone, shale (slate), carbonaceous slate. Bottom bedded chert, high carbonaceous slate, containing pyrite Gray, gray-green sandstone, in the folder gray-green silty slate, slate, and a small amount of carbon-bearing slate

Cambrian

High Beach Group

interpretation, and literature search for historical landslide records (Ozdemir and Altural 2013). One of the key points in assessing landslide susceptibility by means of multivariate statistical models is the selection of the most important conditioning factors (i.e., the predictor variables) (Costanzo et al. 2012; Devkota et al. 2013). The landslide inventory map for the study area was prepared based on aerial photograph, satellite image interpretation, and extensive field surveys. The landslide inventory database for Ganzhou City includes 3971 landslide events (Fig. 1). In the current research, 2979 landslide cases (75 %) out of 3971 detected landslides were randomly selected for modeling, and the remaining 992 (25 %) landslide cases were used for the model validation purposes. The database included accurate address, latitude, and longitude as well as the total population threatened (39,987), total property threatened (19, 363 million Chinese RMB), total volume (7,890,411.4 m3), minimum size (2.4 m3), and maximum size (1,350,000 m3). The collected data confirmed that the area suffered similar landslides in history and at recent times, including some potential threats in the future. We have collected effective factors on landslide susceptibility for Ganzhou City from different databases and departments/organizations. Daily meteorological records during 1960–2012 in Ganzhou from the Jiangxi Province Meteorological Observatory (Table 1) were used in the study. The data included daily precipitation, daily temperature, and rainy days. The 1:50,000 digital elevation model (DEM) and basic geographical information data of Jiangxi Province were prepared from Jiangxi Meteorological Bureau (http://www.weather.org.cn). Geological disaster data were provided by the Department of Land and Resource of Jiangxi Province (http://www.jxgtt.gov.cn). Lithology data were obtained from the Chinese geological land and resources data sharing (http://gsd.cgs.cn/download.asp). The Landsat 7/ETM+ dataset is provided by Geospatial Data Cloud, the Computer Network Information Center, and the Chinese Academy of Sciences (http://www.gscloud.cn).

Upper Sinian Cambrian

Cambrian

Landslide-conditioning factors Geomorphological parameters What are the effective factors on landslide, and which factors are selected in susceptibility assessment? These questions are faced by every researcher. By doing research on the relationships between conditioning factors (related to topographical, geological, and climatic conditions) and the distribution of landslides, we can further understand how landslides work and which factors facilitate the prediction of locations of future landslide occurrence (Yalcin et al. 2011). In the study area, for landslide susceptibility modeling, the following conditioning factors were used: slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index (TWI), slope-length (LS), lithology, normalized difference vegetation index (NDVI), distance from rivers, distance from faults, distance from roads, and rainfall. These factors can best embody the characteristics of the Ganzhou mountain area. Geomorphological parameters used in this study are the slope angle, slope aspect, altitude, plan curvature, profile curvature, TWI, and LS. The digital elevation model (DEM) was created using 25-m interval contours and survey base points representing the altitude values which were extracted from the 1:50,000-scale topographic maps, and the above conditioning factors are shown in Fig. 3a–g, respectively. Using the mentioned DEM, slope angle, slope aspect, and altitude were prepared and mapped in ArcGIS (Fig. 3a–c. Using the System for Automated Geoscientific Analyses (SAGA GIS 2.8), the plan curvature map was prepared (Fig. 3d). In the matter of plan curvature, positive curvature shows convex, zero curvature defines flat, and negative curvature shows concave (Mohammady et al. 2012). Also, profile curvature was calculated, and classified into three groups based on a common standard classification (Pourghasemi et al. 2012b) including < (−0.001), (−0.001)–(0.001), and > (0.001) (Fig. 3e).

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Fig. 3 Topographical parameter maps of the study area: a slope angle, b slope aspect, c altitude, d plan curvature, e profile curvature, f topographic wetness index (TWI), and g slope-length (LS)


Fig. 3 (continued)

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The TWI is a secondary topographic factor within the runoff model which is defined according to the following equation (Moore and Grayson 1991) (Fig. 3f): a TWI ¼ ln ð1Þ tan β where a is the cumulative upslope area draining through a point (per unit contour length), and tan β is the slope angle at the point. The TWI reflects the tendency of water to accumulate at any point in the catchment (in terms of a) and the tendency of gravitational forces to move that water down slope (expressed in terms of tan β as an approximate hydraulic gradient) (Poudyal et al. 2010). In the present study, TWI is divided into three classes (Fig. 3f). Slope-length (LS) is the combination of the slope steepness (S) and slope length (L) which is implemented to represent soil loss potential from the combined slope properties (Fig. 3g). The combined slope-length (LS) factor can be

Fig. 4 Lithology map of the study area

calculated by the equation suggested by Moore and Burch (1986) as follows:

LS ¼

As 22:13

0:6

sinβ 0:0896

1:3

ð2Þ

where As is the specific catchment area (m2/m), and β is the slope angle in degrees. Lithology The underlying geology is part of the most significant factors for landslide modeling (He et al. 2012). Different geology formations have different compositions and structures which contribute to the strength of the material. The stronger rocks give more resistance to the driving forces as compared to the weaker rocks and, hence, are less prone to landslides (Bagherzadeh and Mansouri Daneshvar 2012). The lithology map was obtained from the geological map of the study area (Fig. 4 and Table 1). The lithology structure of the study area includes 13 classes. Based on the geological


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Fig. 5 NDVI map of the study area

China land and resources data sharing (http://gsd.cgs.cn/ download.asp), 19 % of the lithology covering the study area falls within the units described as class L (Lechang Gorge Group) in Table 1, which includes purple gray feldspar quartz sandstone silty slate and pale gray chert and phyllite. Also, 18 % of the study area is covered by classes K (TieShiKou Group) and K (Anyuan Group), which include yellow-green siltstone, calcareous mudstone, and local marl; and sand, shale, chert conglomerate clip the bottom seam, tuffaceous sandstone, tuff, tuffaceous mudstone, oil shale, coal lines, and olivine basalt, respectively.

Normalized difference vegetation index The NDVI map was produced from Landsat 7/ETM+ imagery showing the surface vegetation coverage and density in an image. The NDVI value was computed using following equation and presented in Fig. 5:

. NDVI ¼ ðIR−RÞ ðIR þ RÞ

ð3Þ

where R and IR stand for the spectral reflectance measurements acquired in the visible (red) and near-infrared regions, respectively. Distance to river, distance to fault, and distance to roads Distance to rivers was created using a topographical map, whereas distance to faults map was calculated using a geological map of the study area (Fig. 6a, b). Also distance to roads map was prepared using a road map of the study area (Fig. 6c). All the mentioned maps were calculated based on the Euclidean distance method in ArcGIS 9.3 and were classified into (3000) classes. Rainfall There is no doubt that rainfall is the most important triggering factor in landslides (Duc 2012; Grelle et al. 2013; Chen et al. 2014). There are 17 rainfall stations in the Ganzhou

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Fig. 6 a Distance to river map. b Distance to fault map. c Distance to roads map



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area (Table 2). Based on the rainfall data of the past 52 years (from 1960 to 2012), this area received an average annual rainfall of about 1435 mm. The rainfall distributions in Ganzhou are characterized by a subtropical monsoon humid climate. The highest rainfall normally occurs during the transition period between the spring and summer, and the value of average rainfall is 173.4 mm. Obviously, landslide occurrences showed a good relationship with the rainfall characteristics such as duration, frequency, and intensity. Over 82.5 % of the landslides took place in the spring and summer seasons. These observations showed that there exists a positive correlation between landslide and rainfall occurrences in Ganzhou City. Precipitation ranges from 521.1 to 1669.7 mm in the study area and classified into five categories based on the natural break classification algorithm (Fig. 7). Landslide susceptibility modeling Evidential BELIEF Function model The Dempster-Shafer theory of evidence belief (Dempster 1968; Shafer 1976) is a mathematical-based model with a bivariate statistical methodology, used to find the spatial integration based on the rule of combination. Generally, it is applied successfully as a knowledge-based approach to mineral potential mapping (Carranza 2009). A bivariate EBF was applied to measure the spatial association between the classes of conditioning factors and landslide occurrence. A full discussion was removed and basic information was provided about the algorithm derivation by Althuwaynee et al. (2012). The equation for data-driven estimation of Bel Ci j is as follows (Carranza and Hale 2003): Table 2 Name

Information of weather stations in the study area Longitude (°)

Latitude (°)

Height(m)

Observed time

Chongyi

114.3

25.7

246.9

1958–2012

Shangyou

114.55

25.8

140.7

1959–2012

Nankang

114.75

25.67

127.00

1956–2012

Ganxian

115.00

25.87

137.50

1951–2012

Dayu

114.35

25.40

215.60

1954–2012

Xinfeng

114.93

25.40

164.20

1957–2012

Xingguo

115.35

26.35

151.00

1956–2012

Ningdou

116.02

26.48

209.10

1956–2012

Shicheng

116.35

26.35

247.40

1958–2012

Ruijin

116.03

25.90

208.00

1957–2012

Yudou

115.42

25.97

133.20

1958–2012

Huichang

115.80

25.60

167.40

1956–2012

Anyuan

115.40

25.15

305.00

1958–2012

Quannan

114.53

24.75

252.00

1958–2012

Longnan

114.82

24.92

206.30

1956–2012

Dingnan

115.03

24.78

253.00

1958–2012

Xunwu

115.65

24.95

303.90

1955–2012

WC D BelCi j ¼ X m i j W j¼1 C i j D

ð4Þ

where

W Ci j D

. N C i j ∩D N C i j ¼ . N ðDÞ−N C i j ∩D N ðT Þ−N C i j

ð5Þ

The numerator to estimate parameter W Ci j D in Eq. (5) is the ratio of the conditional probability that D exists given the presence of Cij to the conditional probability that D exists given the absence of Cij. Thus, N(D) is the weight of in terms of D being more present than absent as may be expected due to chance. The degree of belief for Cij, BelCi j , as defined in Eq. (5), is the relative strength of for every jth class of evidence in map Xi. The equation for data-driven estimation of DisCi j is as follows (Carranza and Hale 2003): W DisCi j ¼ X m

Ci j D

ð6Þ

W Ci jD j¼1

where W Ci j D

. N C i j ∩D N C i j ¼ ð7Þ . N ðT Þ−N ðDÞ− N C i j −N C i j ∩D N ðT Þ−N C i j

The W Ci j D in Eq. (7) is the ratio of the conditional probability that D does not exist given the presence of Cij to the conditional probability that D does not exist given the absence of Cij. The W Ci j D thus is the weight of Cij in terms of D being more absent than present as may be expected due to chance. Thus, N(D) the degree of belief for Cij, DisC i j , as defined in Eq. (7), is the relative strength W Ci j D for every jth Cij class of evidence in map Xi. Finally, the values of UncCi j and PlsCi j of the EBFs are explained above. The value of UncCi j measure of uncertainty of D is associated with every Cij in map Xi. The value of PlsCi j is an Boptimistic^ measure of spatial association of D with every Cij in map Xi. The EBF model contains Bel (degree of belief), Dis (degree of disbelief), Unc (degree of uncertainty), and Pls (degree of plausibility) in the range of [0, 1] (Carranza and Hale 2003; Althuwaynee et al. 2012). If no landslide occurs in a certain class, the chances to consider the belief are considerably less than or equal to 0 (Bel = 0). Frequency ratio model According to Bonham-Carter (1994), the frequency ratio is the probability of occurrence of a certain attribute. The frequency

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Fig. 7 The rainfall map of the study area

ratio method is based on the assumption that future landslides will happen at similar conditions to those in the past (Lee and Pradhan 2007). The FR model is a simple and understandable probabilistic model, and it is the ratio of the area where landslides have occurred to the total study area and is also the ratio of the landslide occurrence probabilities to the nonoccurrence for a given attribute (Lee and Pradhan 2007). The landslide susceptibility map (LSM) was calculated by summation of each factor’s ratio value using the following equation: LSM ¼

X

FR

ð8Þ

The calculation step for an FR for a class of the landslideinfluencing factor is shown in Eq. (9): . A B FR ¼ . C D

ð9Þ

where A is the number of pixels with landslide for each factor, B is the number of total landslides in the study area, C is the number of pixels in the class area of the factor, D is the number of total pixels in the study area, and FR is the frequency ratio of a class for the factor. Maximum entropy model Entropy is a measurement of the instability, disorder, imbalance, and uncertainty of a system (Yufeng and Fengxiang 2009; Pourghasemi et al. 2012d, e). The quantity of entropy of a system has a one-to-one relationship with the degree of disorder; this relationship, called the Boltzmann principle, has been used to clarify the thermodynamic status of a system (Yufeng and Fengxiang 2009). The method is based on the principle of bivariate analysis, where the density of landslides within a certain parameter is determined. The information entropy method has been widely used to determine the weight index of natural hazards; besides, it has been used for integrated environmental

Arab J Geosci (2016) 9:112 Table 3 Spatial relationship between conditioning factors and landslide locations using evidential belief function model

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Factor

Class

Belief (Bel)

Disbelief (Dis)

Uncertainty (Unc)

Plausibility (Pls)

Slope degree (°)

0–5 5–15 15–30 >30 1600 Flat

0.214 0.292 0.336 0.158 0.582 0.269 0.101 0.048 0.000 0.069

0.275 0.266 0.181 0.278 0.143 0.316 0.273 0.268 0.000 0.202

0.512 0.441 0.483 0.565 0.276 0.414 0.625 0.685 1.000 0.729

0.725 0.734 0.819 0.722 0.857 0.684 0.727 0.732 1 0.798

North Northeast East Southeast South Southwest West Northwest 12 20 Concave Flat Convex < (−0.001)

0.117 0.124 0.139 0.000 0.151 0.000 0.131 0.122 0.386 0.361 0.253 0.503 0.330 0.166 0.346 0.186 0.468 0.339

0.203 0.201 0.198 0.000 0.196 0.000 0.000 0.000 0.299 0.350 0.351 0.249 0.387 0.364 0.378 0.341 0.280 0.368

0.680 0.675 0.663 1.000 0.653 1.000 0.869 0.878 0.315 0.290 0.395 0.248 0.283 0.469 0.276 0.473 0.252 0.292

0.797 0.799 0.802 1 0.804 1 1 1 0.701 0.650 0.649 0.751 0.613 0.636 0.622 0.659 0.720 0.632

(−0.001)–(0.001) > (0.001) 3000 3000 3000 507.6–931.0

0.233 0.427 0.204 0.207 0.206 0.202 0.000 0.218 0.198 0.212 0.172 0.000 0.192 0.214 0.183 0.162 0.000 0.248

0.338 0.294 0.251 0.250 0.249 0.250 0.000 0.247 0.250 0.247 0.256 0.000 0.249 0.249 0.251 0.252 0.000 0.250

0.428 0.279 0.546 0.543 0.545 0.548 1.000 0.535 0.552 0.542 0.572 1.000 0.559 0.537 0.566 0.587 1.000 0.502

0.662 0.706 0.749 0.750 0.751 0.750 1 0.753 0.750 0.753 0.744 1 0.751 0.751 0.749 0.748 1 0.750

931.0–1093.1 1093.1–1228.3 1228.3–1669.7

0.285 0.244 0.224

0.238 0.254 0.258

0.478 0.502 0.519

0.762 0.746 0.742

Altitude (m)

Slope aspect

TWI

LS (m)

Plan curvature (100/m)

Profile curvature (100/m)

Distance to rivers (m)

Distance to faults (m)

Distance to roads (m)

Precipitation (mm)

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Page 14 of 26

Table 3 (continued) Factor

Class

Belief (Bel)

Disbelief (Dis)

Uncertainty (Unc)

Plausibility (Pls)

NDVI

(−0.92)–(−0.24) (−0.24)–(−0.10) (−0.10)–(−0.01)

0.037 0.126 0.155

0.171 0.170 0.167

0.792 0.704 0.677

0.829 0.830 0.833

(−0.01)–(0.05) (0.05)–(0.12) (0.12)–(0.19) (0.19)–(0.28) (0.28)–(0.35) (0.35)–(0.44) (0.44)–(0.85) A B C D E F G H I J K

0.184 0.000 0.153 0.000 0.087 0.061 0.031 0.080 0.085 0.027 0.050 0.000 0.128 0.000 0.106 0.106 0.075 0.044

0.160 0.000 0.162 0.000 0.000 0.000 0.170 0.200 0.199 0.202 0.204 0.000 0.196 0.000 0.000 0.000 0.000 0.000

0.656 1.000 0.685 1.000 0.913 0.939 0.799 0.720 0.716 0.771 0.745 1.000 0.676 1.000 0.894 0.894 0.925 0.956

0.840 1 0.838 1 1 1 0.830 0.800 0.801 0.798 0.796 1 0.804 1 1 1 1 1

L M

0.056 0.151

0.000 0.000

0.944 0.849

1 1

Lithology

assessments of ecological processes (Yi and Shi 1994; Li et al. 2002). Numerous important factors provide extra entropy into the index system. Therefore, the entropy value can be implemented to calculate quantitative weights of the index system (Pourghasemi et al. 2012d). The equations used to calculate the information coefficient Wj representing the weight value for the parameter as a whole (Constantin et al. 2011; Pourghasemi et al. 2012d; Jaafari et al. 2014) are the following: . Pi j ¼ b a ð10Þ

.X S j Pi j ¼ Pi j P j¼1 i j

Hj ¼ −

XSj j¼1

Pi j log Pi j ; j ¼ 1; …; n

ð11Þ ð12Þ

H jmax ¼ log2 S j ; S j is the number of classes . I j ¼ H jmax −H j H jmax ; I ¼ ð0; 1Þ; j ¼ 1 ; …; n

ð13Þ

W j ¼ I j Pi j

ð15Þ

ð14Þ

where a and b are the domain and landslide percentages, respectively; (Pij) is the probability density; Hj and Hjmax show

entropy values; Hj is the information coefficient; and Wj defines the resultant weight value for the factor as a whole (Bednarik et al. 2010; Devkota et al. 2013). Logistic regression model Regression approaches consisting of linear regression, loglinear regression, and LR have been used commonly (Ozdemir and Altural 2013). LR describes the relationship between a categorical or binary dependent variable and one or more continuous, categorical, or binary explanatory variables derived from samples and yielding the coefficient for each variable. The primary goal of LR is to find the best model to describe the relationship between a dependent variable and multiple independent variables (Ozdemir and Altural 2013). The advantage of logistic regression is that, through the addition of an appropriate link function to the usual linear regression model, the variables may be either continuous or discrete or any combination of both types, and they do not necessarily have normal distributions (Pradhan and Lee 2010). The independent variables in this model can be valued as 0 and 1, representing the absence and presence of landslide cells or sites; model outcomes between 0 and 1 show the potential of the landslide susceptibility. Detailed descriptions of the logistic regression technique can be found in Hosmer and Lemeshow (2000) and


Fig. 8 Integrated results of evidential belief function model: a belief, b disbelief, c uncertainty, and d plausibility

Page 15 of 26 112

112


Page 16 of 26

Table 4

Frequency ratio and maximum entropy values of landslide-conditioning factors

Factor

Class

No. of pixels in domain

No. of landslides

% Pixels in domain

% Landslides

FR

Maxent values

Slope angle (°)

0–5 5–15 15–30 >30 1600 Flat North Northeast East Southeast South Southwest West Northwest 12 20 Concave Flat Convex < (−0.001) (−0.001)–(0.001) > (0.001) 3000 3000 3000 507.6–931.0 931.0–1093.1 1093.1–1228.3 1228.3–1669.7 (−0.92)–(−0.24) (−0.24)–(−0.10) (−0.10)–(−0.01) (−0.01)–(0.05) (0.05)–(0.12) (0.12)–(0.19) (0.19)–(0.28) (0.28)–(0.35) (0.35)–(0.44) (0.44)–(0.85) A

6,395,928 2,914,581 48,563,124 5,273,713 41,820,074 19,138,218 1,968,506 209,306 11,242 1,643,579 7,274,665 7,296,582 8,055,871 8,132,853 7,567,770 7,399,296 7,881,351 7,895,379 58,569,111 3,140,580 1,437,655 50,011,470 12,365,059 770,817 28,038,949 1,302,600 33,805,797 28,942,250 1,184,205 33,020,891 6,749,346 5,854,326 10,551,398 9,476,081 30,516,195 7,532,465 6,857,641 11,851,057 9,438,537 27,467,646 1,334,316 1,288,342 2,540,699 2,528,425 55,455,564 8,269,429 17,048,795 23,891,751 13,937,371 267,203 725,527 3,060,895 6,194,759 7,628,663 8,938,405 11,046,035 10,355,267 9,833,717 5,096,875 4,627,099

210 131 2510 128 2441 517 20 1 0 40 301 320 396 421 403 392 366 340 2794 140 45 2552 414 13 1122 28 1829 1209 34 1736 335 295 531 467 1351 387 320 591 383 1298 50 54 91 80 2704 385 912 1096 586 4 37 192 462 510 554 551 364 241 64 232

10.13 4.62 76.90 8.35 66.23 30.31 3.12 0.33 0.02 2.60 11.52 11.55 12.76 12.88 11.98 11.72 12.48 12.50 92.75 4.97 2.28 79.20 19.58 1.22 44.40 2.06 53.53 45.83 1.88 52.29 10.69 9.27 16.71 15.01 48.33 11.93 10.86 18.77 14.95 43.50 2.11 2.04 4.02 4.00 87.82 13.10 27.00 37.83 22.07 0.42 1.15 4.85 9.81 12.08 14.15 17.49 16.40 15.57 8.07 7.33

7.05 4.40 84.26 4.30 81.94 17.35 0.67 0.03 0.00 1.34 10.10 10.74 13.29 14.13 13.53 13.16 12.29 11.41 93.79 4.70 1.51 85.67 13.90 0.44 37.66 0.94 61.40 40.58 1.14 58.27 11.25 9.90 17.82 15.68 45.35 12.99 10.74 19.84 12.86 43.57 1.68 1.81 3.05 2.69 90.77 12.92 30.61 36.79 19.67 0.13 1.24 6.45 15.51 17.12 18.60 18.50 12.22 8.09 2.15 7.79

0.70 0.95 1.10 0.51 1.24 0.57 0.22 0.10 0.00 0.52 0.88 0.93 1.04 1.10 1.13 1.12 0.98 0.91 1.01 0.94 0.66 1.08 0.71 0.36 0.85 0.46 1.15 0.89 0.61 1.11 1.05 1.07 1.07 1.04 0.94 1.09 0.99 1.06 0.86 1.00 0.79 0.89 0.76 0.67 1.03 0.99 1.13 0.97 0.89 0.32 1.08 1.33 1.58 1.42 1.31 1.06 0.75 0.52 0.27 1.06

0.023

Altitude (m)

Slope aspect

TWI

LS (m)

Plan curvature (100/m) Profile curvature (100/m) Distance to rivers (m)

Distance to faults (m)

Distance to roads (m)

Precipitation (mm)

NDVI

Lithology

0.149

0.009

0.012

0.058

0.047

0.023

0.001

0.002

0.006

0.003

0.052

0.027


Page 17 of 26 112

Table 4 (continued) Factor

Class

No. of pixels in domain

No. of landslides

% Pixels in domain

% Landslides

FR

B C D E F G H I J K L M

4,384,430 349,276 2,906,521 6,786,732 2,191,717 2,388,906 3,050,118 2,228,541 9,766,521 11,429,278 12,209,870 828,337

234 6 92 386 176 108 202 148 596 317 432 50

6.94 0.55 4.60 10.75 3.47 3.78 4.83 3.53 15.47 18.10 19.34 1.31

7.85 0.20 3.09 12.96 5.91 3.63 6.78 4.97 20.01 10.64 14.50 1.68

1.13 0.36 0.67 1.21 1.70 0.96 1.40 1.41 1.29 0.59 0.75 1.28

Kleinbaum and Klein (2010). The logistic model can be expressed according to Eq. (16): . q ¼ expðT Þ ð1 þ expðT ÞÞ ð16Þ where q represents the probability of landslide susceptibility, which varies from 0 to 1 on an S-shaped curve; T is defined by the following equation (linear logistic model), and its value varies from −∞ to +∞ (Eq. 17): T ¼ α0 þ α1 X 1 þ α2 X 2 þ … þ αn X n

ð17Þ

where α0 represents the intercept of the model; 1, 2, … , n represent the partial regression coefficients; and X1, X2, … , Xn show the independent variables (numbers, n). The logistic regression model includes the fitting of Eq. (16) to the data and then expressing the probability of the presence/absence of landslides in each mapping unit. The relative contribution of each mapping unit to the logistic function can be acquired by looking at the significance of each regression parameter. The probability, q, always rises when the value of T increases. The regression coefficients α1, α2…, αn show the contribution of each explanatory variable to the probability value, q * A positive sign shows that the explanatory variable has increased the probability of change, and a negative sign represents the opposite effect. The logistic regression analysis was performed using the Statistical Package for Social Sciences (SPSS) statistical software. Firstly, all the conditioning factors and landslide locations were converted into grid format. Then, these grid maps were converted into ACSII data format (acronym for the American Standard Code for Information Interchange). ASCII data of each map were exported to ArcGIS 9.3 and analyzed with SPSS statistical software, and the logistic regression model was run to obtain the coefficients of the landslide-conditioning factors (Devkota et al. 2013).

Maxent values

Validation of landslide susceptibility maps To determine the accuracy of the four landslide susceptibility maps created in the current research using EBF, FR, Maxent, and LR models, the ROC curve was used (Akgun et al. 2012a, b; Pourghasemi et al. 2012b; Ozdemir and Altural 2013; Jaafari et al. 2015; Pourtaghi and Pourghasemi 2014; Naghibi and Pourghasemi 2015). The ROC can be represented equivalently by plotting the fraction of true positives out of the positives versus the fraction of false positives out of the negatives, for a binary classifier system as its discrimination threshold is varied by tradition. The plot shows the falsepositive rate (1 specificity) on the x-axis (Eq. 18) and the true-positive rate (sensitivity or 1—the false negative rate) on the y-axis (Eq. 19) (Swets 1988). To apply the ROC method for the study area, a concise and representative dataset was prepared by using pixels from the landslide location (992 pixels) and pixels from the randomly selected nonlandslide locations (992 pixels) in the investigated area. The AUC varies from 0.5 to 1.0. The model with higher AUC is considered to be the best. If the area under the ROC curve (AUC) is close to 1, the result of the test is excellent. On the other hand, if the model does not predict well, then this value will be close to 0.5. TN χ ¼ 1 ¼ specificity ¼ 1− ð18Þ ðTN þ FPÞ TN γ ¼ sensitivity ð19Þ ðTp þ FNÞ

Results Application of evidential belief function The spatial factor datasets were evaluated using EBFs to reveal the correlation between the existing landslides and the

112


Page 18 of 26

Table 5 Beta coefficients and test statistics of the variables used in the logistic regression equation

Factors

B

Standard error

Slope aspect Flat North Northeast East

−1.085 −0.140 0.096 0.213

0.238 0.117 0.117 0.113

Southeast South Southwest West Northwest Slope degree Altitude TWI Plan curvature Profile curvature Distance to rivers Distance to faults Distance to roads Precipitation NDVI Lithology A B

0.416 0.390 0.258 −0.079 0.25 0.000325 −0.003 0.006 0.328 0.266 0.000064 0.000009 0.000024 −0.00004 −2.977

C D E F G H I J K L M Constant

Degrees of freedom

Significant

Exp (B)

20.795 1.435 0.665 3.566

1 1 1 1

0.000 0.231 0.415 0.059

0.338 0.870 1.100 1.237

0.114 0.115 0.115 0.112 0.111 0.004 0.000 0.008 0.049 0.044 0.000 0.000 0.000 0.000 0.207

13.423 11.539 5.081 0.504 0.428 0.008 140.426 0.623 45.819 36.650 19.819 1.076 89.294 0.040 207.841

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0.000 0.001 0.024 0.478 0.015 0.930 0.000 0.430 0.000 0.000 0.000 0.300 0.000 0.842 0.000

1.516 1.477 1.295 0.924 0.426 1.000 0.997 1.006 1.389 1.305 1.000 1.000 1.000 1.000 0.051

−0.116 −0.161

0.253 0.251

0.211 0.413

1 1

0.646 0.521

0.890 0.851

−1.340 −0.449 0.006 0.502 −0.303 0.202 0.037 0.143 −0.752 −0.421 0.158 0.919

0.542 0.268 0.244 0.266 0.270 0.259 0.265 0.239 0.246 0.238 0.059 0.344

6.104 2.808 0.001 3.553 1.257 0.605 0.020 0.358 9.373 3.114 0.134 7.148

1 1 1 1 1 1 1 1 1 1 1 1

0.013 0.094 0.982 0.059 0.262 0.437 0.888 0.550 0.002 0.078 0.053 0.008

0.262 0.639 1.006 1.652 0.739 1.224 1.038 1.153 0.471 0.656 0.056 2.507

characteristic spatial factors in the study area. Table 3 shows the estimated EBFs (belief, disbelief, uncertainty, and plausibility). In the case of slope angle, 15–30 classes had the highest belief. On slope aspect, the degree of belief was higher for slopes with south and east aspects. The results showed that there was an inverse relationship between altitude and belief. In the case of plan curvature, the convex class showed the highest amount of belief, while the flat class represented the lowest amount of belief. According to the results, the > (0.001) class of profile curvature had the most belief. In the case of TWI and LS, there was an inverse relationship between them and belief degree. In the case of lithology, there were 13 classes. The degree of belief, with respect to landslide occurrence, was higher for the M class but

Wald

lower for the E and G classes. In the case of land use and NDVI, the degree of belief was higher for the (−0.01)–(0.05) class but lower for the (0.05)–(0.12) and (0.19)–(0.28) classes. Distance to rivers, distance to faults, and distance to roads also showed indirect relationships with landslide occurrences according to the degrees of belief. The estimated degrees of belief and degrees of disbelief for the precipitation indicated that, relative to other classes, important for landslide occurrence was higher for 931– 1093 classes. The integrated results of evidential belief function model are shown in Fig. 8. The belief map (Fig. 8a) was compared to the disbelief map (Fig. 8b) which showed that belief values were high for areas where disbelief values are low and vice versa. It

Arab J Geosci (2016) 9:112 Table 6 Multicollinearity of conditioning factors in the study area

Page 19 of 26 112

Model

1

Unstandardized coefficients

Standardized coefficients

t

Sig.

Collinearity statistics

B

Standard error

Beta

(Constant)

0.656

0.057

Slope aspect

0.004

0.003

0.017

1.347

0.178

0.966

1.035

Slope degree

0.001

0.001

0.016

1.153

0.249

0.752

1.330

Tolerance 11.596

VIF

0.000

Altitude

0.000

0.000

−0.226

−13.545

0.000

0.547

1.830

TWI

−0.002

0.002

−0.017

−1.051

0.293

0.608

1.644

Plan curvature

0.068

0.011

0.101

6.486

0.000

0.632

1.582

Profile curvature

0.064

0.010

0.097

6.727

0.000

0.736

1.358

Distance from river Distance from faults Distance from roads Rainfall

1.479E-5

0.000

0.071

4.734

0.000

0.685

1.460

3.383E-6

0.000

0.022

1.732

0.083

0.963

1.039

5.932E-6

0.000

0.159

11.407

0.000

0.786

1.273

9.711E-6

0.000

0.003

0.223

0.824

0.931

1.075

NDVI

−0.538

0.043

−0.167

−12.584

0.000

0.871

1.149

Lithology

−0.006

0.002

−0.045

−3.646

0.000

0.986

1.014

showed that high landslide susceptibility was for the areas where there were high degrees of belief and a low degree of disbelief for the occurrence. The uncertainty map (Fig. 8c) showed a lack of information to provide a real proof for landslide susceptibility. High uncertainty values were in the areas where belief values were low. The plausibility map (Fig. 8d) shows high values for areas where both belief and uncertainty values are high. Our results are in agreement with Carranza and Hale (2003), Carranza et al. (2008), Tien Bui et al. (2012a, b), Lee et al. (2012), Althuwaynee et al. (2014), Nampak et al. (2014), Pradhan et al. (2014), and Pourghasemi and Beheshtirad (2014). Application of the frequency ratio model The results of the FR analysis for each identified class are summarized in Table 4. In Table 4, slope angle classes showed that the 15–30° class has a higher frequency ratio weight (1.10) followed by class 5–15° with FR of 0.95. In terms of slope aspect, most landslides occurred facing south, southwest, and southeast (1.13, 1.12, and 1.10, respectively). In the case of altitude, the (0.001) class had the highest FR. Therefore, this class has the most probability for landslide occurrence. The results of TWI showed that