ORIGINAL PAPER Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran Hamid Reza Pourghasemi & Biswajeet Pradhan & Candan Gokceoglu & Majid Mohammadi & Hamid Reza Moradi Received: 4 September 2011 /Accepted: 8 February 2012 /Published online: 9 March 2012 # Saudi Society for Geosciences 2012 Abstract The main goal of this study was to investigate the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. The landslide conditioning factors considered for the study area were slope gradient, slope aspect, altitude, lithology, land use, distance from streams, distance from roads, distance from faults, topo- graphic wetness index, stream power index, stream transport index and plan curvature. For validation of the produced landslide susceptibility maps, the results of the analyses were compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all the landslide susceptibility models were constructed and the areas under the curves were calculated. The landslide locations were used to validate results of the landslide susceptibility maps. The verification results showed that the weights-of-evidence model (79.87%) performed better than certainty factor (72.02%) model with a standard error of 0.0663 and 0.0756, respectively. According to the results of the area under curve evaluation, the map produced by weights-of-evidence exhibits satisfactory properties. Keywords Landslide susceptibility .Weights of evidence . Certainty factor model . GIS . Remote sensing . Iran Introduction Landslides are one of the most catastrophic natural hazards occurring in many areas of the world. Globally, they cause hundreds of billions of dollars in damage and hundreds of thousands of deaths and injuries each year (Aleotti and Chowdhury 1999). Over the past 25 years, many govern- ments and research institutions throughout the world have invested considerable resources in assessing landslide hazards and in attempting to produce maps portraying their spatial distribution (Guzzetti et al. 1999). In spite of improvements in recognition, mitigative measures, and prediction and warning systems, landslide damage is still increasing worldwide (Schuster 1996). Losses resulting from mass movements in Iran until the end of September 2007 have been estimated at 126,893 billion Iranian Rials (about USD 12,700 million) using the 4,900 land- slide database. Similar approaches have been proposed by several investigators, including weights-of-evidence methods H. R. Pourghasemi :M. Mohammadi :H. R. Moradi Department of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modares University International Campus, Noor, Iran e-mail: hm_porghasemi@yahoo.com B. Pradhan (*) Institute of Advanced Technology, Spatial and Numerical Modelling Laboratory, University Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia e-mail: biswajeet24@gmail.com B. Pradhan e-mail: biswajeet@mailcity.com C. Gokceoglu Applied Geology Division, Department of Geological Engineering, Engineering Faculty, Hacettepe University, Ankara, Turkey Arab J Geosci (2013) 6:2351–2365 DOI 10.1007/s12517-012-0532-7 (Bonham-Carter 1991; Lee et al. 2002a; Wu et al. 2004; Gokceoglu et al. 2005; Neuhäuser and Terhorst 2007; Mathew et al. 2007; Bui et al. 2008; Zhu and Wang 2009; Regmi et al. 2010; Oh and Lee 2011), weighting factors (Çevik and Topal 2003), weighted linear combinations of instability factors (Ayalew et al. 2004), landside nominal risk factors (Gupta and Joshi 1990; Saha et al. 2005), probabilistic models (Chung and Fabbri 2003, 2005; Lee 2004; Lee and Pradhan 2006, 2007; Akgun et al. 2011; Pradhan et al. 2012), certainty factors (Binaghi et al. 1998), information values (Lin and Tung 2004; Saha et al. 2005), modified Bayesian estimation (Chung and Fabbri 1998) and data mining (Biswajeet and Saied 2010; Pradhan et al. 2009, 2010a, b, c, d, e, 2011 Pradhan 2010a, b, c, 2011a, b Pradhan and Lee 2010a, b; Pradhan and Buchroithner 2010; Pradhan and Youssef 2010; Sezer et al. 2011; Oh and Pradhan 2011; Bui et al. 2011; Akgun et al. 2012). Understanding the differences between the proposed approaches is not always simple. The main differences are the rigour of the ap- proach (Chung and Fabbri 1998) and the method used to estimate the prior probability of landslide occurrence. The aim of the present study was to produce landslide susceptibil- ity maps of the Haraz watershed in Iran by employing a weights-of-evidence and certainty factor models. Study area The study area is located in the northern part of Iran, which is one of the most landslide-prone areas in the country (Pourghasemi 2008). The watershed area lies between longitudes 52° 06′ 02″ E and 52° 18′ 13″ E and between latitudes 35° 49′ 05″ N and 35° 57′ 39″ N. It is mountainous and is located in the Alborz Folded geological zone (Fig. 1). It covers two adjacent 1:50,000 topographic sheets of the Army Geographic Institute of Iran and has an extent of Fig. 1 Location map of the study area in Mazandaran province, Iran 2352 Arab J Geosci (2013) 6:2351–2365 about 114.5 km2. The main river in the study area is the Haraz River. The temperature varies between 25°C in winter and 36.5°C in summer. The mean annual rainfall is around 500 mm, most of which falls between November and January. The altitudes in the study area vary between 1,200 and 3,290 m. The slope angles of the area range from 0° to as much as 70°. The majority of the area (64.82%) is covered by moderate pasture. The other parts of the study area are utilised for orchard and agricultural (13.33%), residential (0.3%) and best pasture purposes (21.55%). Weights-of-evidence model In recent years, many investigators (Bonham-Carter 1991; Mathew et al. 2007; Neuhäuser and Terhorst 2007; Bui et al. 2008; Regmi et al. 2010; Pradhan et al. 2010c) have experimented with methods that exploit, more or less rigorously, Bayes’ conditional probability theorem. In this framework, conditional probability is a measure of the chance of a hypothesis being true or false given a piece of evidence (Gorsevski et al. 2003). For example, Bayesian probabilistic modelling is supplied for solving problems of decision-making under uncertainties. This method is suitable for landslide susceptibility mapping because its uncertainty is connected with landslide events and their associations with the complex landscape (Chung and Fabbri 1998; Gorsevski et al. 2003). Bayes’ theorem can be written as (Guzzetti 2005): P AjBð Þ ¼ P BjAð Þ � PðAÞ PðBÞ ð1Þ So, the probability of phenomena B occurring given that phenomena A has occurred, P(B|A), multiplied by the prob- ability of phenomena A occurring, P(A), and divided by the probability of phenomena B occurring, P(B). In Eq. 1, P(A) is the “prior probability” (i.e. a reasonable hypothesis on the probability of phenomena A), P(B) is the “posterior probability” (i.e. the probability of B under all possible outcomes for A), and P(A|B) is the “probability” (i.e. the conditional probability of A given B). In a best Bayesian analysis, the prior probability has a minor effect on the posterior probability, as most of the information comes from the likelihood. When applied to landslide susceptibility investigation, Bayes’ theorem is used to select the probability that an area will improve slope failures given the local environmental circumstances, as indicated in Eq. 2 (Chung and Fabbri 1998): P ALj V0ðrÞf ;V1ðrÞ . . . ;VmðrÞgð Þ ¼ P V0ðrÞf ;V1ðrÞ . . . ;VmðrÞgjALð Þ � P ALð Þ P V0ðrÞ;V1ðrÞ; . . . ;VmðrÞð Þ ð2Þ where, AL denotes area of landslide in a mapping unit r for which V0ðrÞf ;V1ðrÞ . . . ;VmðrÞg is independent of environmental conditions. Additionally, the mixture of environmental conditions is special to the mapping unit r. Equation 2 showed that the probability that a mapping Fig. 2 The landslide inventory map of the study area Arab J Geosci (2013) 6:2351–2365 2353 unit r in the study area will be influenced by a landslide which is equivalent to the probability of a landslide in the study area, P(AL), multiplied by the probability of a particular (unique) mixture of environmental factors given the presence of a landslide, divided by the probability of the same mixture of environmental factors in the whole study area. A simple strategy is to acquire the three probabilities in the right- hand side of Eq. 2 in a geographic information system (GIS) from the related spatial densities. These probabilities can be obtained as follows: (1) by dividing the entire AL in the study area by the area of the mapping unit, for P(AL); (2) by dividing the whole area of the unique condition unit by the extent of the study area for P V0ðrÞ;V1ðrÞ; . . . ;VmðrÞð Þ; and (3) by consid- ering the percentage of the landslide area in the study area Fig. 3 Lithology map of the study area Table 1 Description of geological units of the study area No. Symbol Formation Lithology Geological age A Q sc – Scree Quaternary Qt 2 – Young terraces Quaternary Qt 1 – Old terraces Quaternary B Q ag – Agglomerate Quaternary Q ta – Trachy andesitic lava flows Quaternary Q tu – Ash tuff, lapilli tuff Quaternary Q b – Olivine basalt Quaternary C K tv k Karaj Green tuff, basaltic and limestone with gypsum and conglomerate Eocene Egy k Karaj Gypsum Eocene D PEz Ziarat Limestone bearing nummulites and alveolina, conglomerate Paleocene PEf Fajan Conglomerate, agglomerate, some marl and limestone Paleocene E K2 – Biogenic and cherty limestone Late Cretaceous Kt Tizkuh Orbitoline bearing limestone Late Cretaceous J1 Lar Massive to well bedded, cherty limestone Late Jurassic Jd Dalichai Well bedded, partly oolitic-detritic limestone, marly limestone Late Jurassic JS Shemshak Dark shale and sandstone with plant remains, coal Late Jurassic TReL Elika Thin bedded limestone Early Triassic Pd Dorud Cross-bedded, quartzitic sandstone Early Permian 2354 Arab J Geosci (2013) 6:2351–2365 characterised by the total area of the unique environmental setting in Eq. 2, for P V0ðrÞf ;V1ðrÞ . . . ;VmðrÞgjALð Þ. An advantage of Bayesian probabilistic modelling is the possibility of incorporating uncertainty into the susceptibil- ity model and considering expert knowledge explicitly (Chung and Fabbri 1998). Certainty factor model Among the commonly used GIS analysis models for landslide susceptibility, certainty factor (CF) model has been widely considered and experimentally investigated in the literature (Chung and Fabbri 1993; Binaghi et al. 1998; Luzi and Pergalani 1999; Lan et al. 2004; Kanungo et al. 2011). The CF approach is one of the possible proposed favourability functions to handle the problem of combination of different data layers and the heterogeneity and uncertainty of the input data. The main difference is the bivariate model with other models of how to combine the maps. Thus, the maps classifieds and then weight of each pixel is obtained using Eq. 3: CF ¼ PPa�PPs PPa 1�PPsð Þ if PPa � PPs PPa�PPs PPs 1�PPað Þ if PPa < PPs 8< : ð3Þ where, PPa is the conditional probability of landslide event occurring in class a and PPs is the prior probability of total number of landslide events in the study area A. With the use of the CF model, each class or area is assigned a value that varies within the interval [−1, 1]. A positive value means a growth in the certainty of the landslide occurrence, whereas a negative value coincides with a decrease in the certainty of landslide occurrence. A value close to 0 means that there is not enough information about the variable and thus, it is difficult to give information about the certainty of landslide occurrence. The CF values are calculated for all condition factors by overlaying and calculating the landslide frequency as given then the CF values of all parameters in 12 landslide condi- tioning factors are determined using Eq. 3. Next, the CF values of the landslide conditioning factor are pairwise com- bined using the CF combination rule. A combination of two CF values, X and Y from two different layers of information is a CF value Z obtained as follows (Chung and Fabbri 1993; Binaghi et al. 1998; Luzi and Pergalani 1999): Z ¼ X þ Y � XY X ; Y � 0 XþY 1�min Xj j; Yj jð Þ X ; Y opposite sign X þ Y þ XY X ; Y < 0 8< : ð4Þ The pairwise combination by using the integration rule of Eq. 4 is performed repeatedly until all the CF layers are combined to obtain the landslide susceptibility. Thematic data preparation Various thematic data layers representing landslide condition- ing factors, such as slope gradient, slope aspect, altitude, lithol- ogy, land use, distance to faults, distance to streams, distance to roads, topographic wetness index (TWI), stream power index (SPI), stream transport index (STI) and plan curvature were prepared. These factors fall under the category of preparatory factors, which make the area susceptible to movement without actually initiating a landslide; thus, these factors are considered to be responsible for the occurrence of landslides in the regions for which pertinent data can be collected from available resour- ces and from the field. The triggering factors, such as rainfall and earthquake, set the movement off by shifting the slope from a marginally stable to an actively unstable area. Further- more, the attributes of the ground in terms of landslide suscep- tibility were considered in the present study. Since, past data on triggering factors such as rainfall and earthquakes in relation to landslide occurrences were not available. Consequently, these factors were not considered in this study. Landslide inventory map The mapping of existing landslides is essential for studying the relationship between the landslide distribu- tion and the conditioning factors. To produce a detailed and reliable landslide inventory map, extensive field surveys and observations were performed in the study area. A total of 78 landslides were identified and mapped in the study area by evaluating aerial photos in 1:25,000 scale and by field survey (Fig. 2). The modes of failure for the landslides identified in the Fig. 4 Land use map of the study area Arab J Geosci (2013) 6:2351–2365 2355 study area were recognised as rotational slides according to the landslide classification system proposed by Varnes (1978). Of the 78 landslides identified randomly, 55 (70%) locations were chosen for the landslide susceptibility maps, while the remaining 23 (30%) cases were used for the model validation. Fig. 5 Topographical parameter maps of the study area; a slope gradient, b slope aspect, c altitude, d plan curvature, e topographic wetness index, f stream power index, g sediment transport capacity index 2356 Arab J Geosci (2013) 6:2351–2365 Landslide conditioning factors A geology map of the study area (1:100,000 series, sheet number 6,461, prepared by Geological Survey of Iran) was digitised in the ILWIS 3.3 software environment. The study area is covered by various types of lithological formations, such as Quaternary, Eocene, Paleocene, late Cretaceous, late Jurassic, early Triassic and early Permian. The Quaternary deposits cover about 40% of the study area. The general geological setting of the area is shown in Fig. 3 and the lithological properties are summarised in Table 1. Four different types of land use were described for this study using a supervised classification and field surveys of ETM+ (2006) satellite images (Youssef et al. 2009, 2012). These types of land use were moderate pasture, best pasture, mixing orchard and agricultural and residential areas (Fig. 4). Most part of the study area (64.82%) is covered by moderate pasture. Consequently, best pasture, mixing orchard and agriculture and residential areas are covered by 21.55%, 13.33%, and 0.30% of the study area, respec- tively. A digital elevation model (DEM) was created using the topographic database. The slope gradient, slope aspect, plan curvature and three common secondary geomorpho- metric parameters that are relevant to the landslide analysis were calculated from the DEM. The sediment transport capacity index (LS) (Moore et al. 1988), SPI (Moore and Grayson 1991) and TWI (Moore and Grayson 1991) were derived from the DEM using the script written by Hengl et al. (2003), which was executed using the ILWIS 3.3 software (Fig. 5). The distances of the rivers, roads and faults were also digitised from the 1:50,000 and 1:100,000 topographic maps and the geological maps, respectively (Fig. 6). Landslide susceptibility maps and their validation Due to their hazardous character, many government and research institutions throughout the world have attempted to assess landslide susceptibility, hazards, and risks and to show their spatial pattern over the years. In this research, both weights-of-evidence and CF models were used for identifying the areas susceptible to landslides at the Haraz Mountains of Iran. A total of 78 landslides were mapped using aerial photographs and subsequent field survey. The landslide conditioning factors considered included slope gradient, slope aspect, altitude, lithology, land use, distance to streams, distance to roads, distance to faults, TWI, SPI, Fig. 6 a Distances from rivers, b distances from roads, c distances from faults Arab J Geosci (2013) 6:2351–2365 2357 T ab le 2 S pa tia l re la tio ns hi p be tw ee n ea ch fa ct or an d la nd sl id e by w ei gh ts -o f- ev id en ce m od el F ac to r C la ss N o. of pi xe ls in do m ai n P er ce nt ag e of do m ai n N o. of la nd sl id e P er ce nt ag e of la nd sl id e W + W − C S2 (W + ) S2 (W − ) S (C ) C /S (C ) S lo pe gr ad ie nt (i n de gr ee ) 0– 5 13 ,8 51 1. 21 1 1. 82 0. 41 − 0. 00 0. 41 1 0. 02 1. 01 0. 41 6– 15 64 ,2 68 5. 62 2 3. 64 − 0. 43 0. 02 − 0. 46 0. 5 0. 02 0. 72 − 0. 63 16 – 30 15 5, 60 2 13 .5 9 10 18 .1 8 0. 29 − 0. 05 0. 34 0. 1 0. 02 0. 35 0. 99 31 – 50 34 3, 63 4 30 .0 3 19 34 .5 5 0. 14 − 0. 07 0. 21 0. 05 0. 03 0. 28 0. 73 51 − 70 26 2, 11 7 22 .9 1 10 18 .1 8 − 0. 23 0. 06 − 0. 29 0. 1 0. 02 0. 35 − 0. 83 > 70 30 4, 80 9 26 .6 4 13 23 .6 4 − 0. 12 0. 04 − 0. 16 0. 08 0. 02 0. 32 − 0. 50 S lo pe as pe ct N or th 14 9, 99 7 13 .1 2 5 9. 09 − 0. 37 0. 05 − 0. 41 0. 2 0. 02 0. 47 − 0. 88 N or th ea st 19 5, 30 1 17 .0 7 9 16 .3 6 − 0. 04 0. 01 − 0. 05 0. 11 0. 02 0. 36 − 0. 14 E as t 12 9, 16 7 11 .2 9 2 3. 64 − 1. 13 0. 08 − 1. 22 0. 5 0. 02 0. 72 − 1. 69 S ou th ea st 17 1, 14 4 14 .9 5 16 29 .0 9 0. 67 − 0. 18 0. 85 0. 06 0. 03 0. 30 2. 85 S ou th 13 5, 67 7 11 .8 5 3 5. 46 − 0. 78 0. 07 − 0. 85 0. 33 0. 02 0. 59 − 1. 43 S ou th w es t 13 1, 71 8 11 .5 1 9 16 .3 6 0. 35 − 0. 06 0. 41 0. 11 0. 02 0. 36 1. 12 W es t 79 ,9 79 6. 99 7 12 .7 3 0. 60 − 0. 06 0. 66 0. 14 0. 02 0. 40 1. 64 N or th w es t 15 1, 29 8 13 .2 2 4 7. 27 − 0. 60 0. 07 − 0. 66 0. 25 0. 02 0. 52 − 1. 28 A lti tu de (m ) 1, 20 0– 1, 50 0 28 ,4 63 2. 49 0 0 N on e 0. 03 0 N on e 0. 02 N on e N on e 1, 50 0 – 1, 80 0 15 7, 01 8 13 .7 2 19 34 .5 4 0. 92 − 0. 28 1. 20 0. 05 0. 03 0. 28 4. 23 1, 80 0– 2, 10 0 30 3, 05 8 26 .4 8 22 40 0. 41 − 0. 20 0. 62 0. 05 0. 03 0. 28 2. 24 2, 10 0– 2, 40 0 30 5, 84 4 26 .7 3 7 12 .7 3 − 0. 74 0. 17 − 0. 92 0. 14 0. 02 0. 40 1 − 2. 27 2, 40 0– 2, 70 0 20 8, 32 1 18 .2 0 6 10 .9 1 − 0. 51 0. 09 − 0. 60 0. 17 0. 02 0. 43 1 − 1. 38 2, 70 0– 3, 00 0 12 5, 38 4 10 .9 6 1 1. 82 − 1. 80 0. 10 − 1. 89 1 0. 02 1. 01 − 1. 88 > 3, 00 0 16 ,1 93 1. 42 0 0 N on e 0. 01 0 N on e 0. 02 N on e N on e L ith ol og y A 45 9, 91 4 40 .1 9 30 54 .5 5 0. 31 − 0. 27 0. 58 0. 03 0. 04 0. 27 2. 14 B 15 3, 62 1 13 .4 3 3 5. 45 − 0. 90 0. 09 − 0. 99 0. 33 0. 02 0. 59 − 1. 67 C 14 7, 38 6 12 .8 8 2 3. 64 − 1. 26 0. 10 − 1. 37 0. 5 0. 02 0. 72 − 1. 90 D 19 ,6 55 1. 72 0 0 N on e 0. 02 0 N on e 0. 02 N on e N on e E 36 3, 70 5 31 .7 8 20 36 .3 6 0. 13 − 0. 07 0. 20 0. 05 0. 03 0. 28 0. 73 L an d us e G oo d pa st ur e 24 6, 60 1 21 .5 5 12 21 .8 2 0. 01 − 0. 00 3 0. 02 0. 08 0. 02 0. 33 0. 05 M ix or ch ar d an d ag ri cu ltu re 15 2, 51 8 13 .3 3 20 36 .3 6 1. 00 − 0. 31 1. 31 0. 05 0. 03 0. 28 4. 68 R es id en tia l 3, 45 0 0. 30 1 1. 82 1. 80 − 0. 02 1. 81 1 0. 02 1. 01 1. 80 M id dl e pa st ur e 74 1, 71 2 64 .8 2 22 40 − 0. 48 0. 53 − 1. 02 0. 05 0. 03 0. 28 − 3. 69 D is ta nc e to fa ul ts (m ) B uf fe r (1 00 m ) 44 ,9 42 3. 93 3 5. 45 0. 33 − 0. 02 0. 34 0. 33 0. 02 0. 59 0. 58 B uf fe r (2 00 m ) 43 ,1 32 3. 77 4 7. 27 0. 66 − 0. 04 0. 69 0. 25 0. 02 0. 52 1. 34 B uf fe r (3 00 m ) 43 ,1 44 3. 77 6 10 .9 1 1. 06 − 0. 08 1. 14 0. 17 0. 02 0. 43 2. 63 B uf fe r (4 00 m ) 44 ,9 14 3. 92 2 3. 64 − 0. 08 0. 00 3 − 0. 08 0. 5 0. 02 0. 72 − 0. 11 2358 Arab J Geosci (2013) 6:2351–2365 T ab le 2 (c on tin ue d) F ac to r C la ss N o. of pi xe ls in do m ai n P er ce nt ag e of do m ai n N o. of la nd sl id e P er ce nt ag e of la nd sl id e W + W − C S2 (W + ) S2 (W − ) S (C ) C /S (C ) B uf fe r (> 40 0 m ) 96 8, 14 9 84 .6 1 40 72 .7 3 − 0. 15 0. 57 − 0. 72 0. 03 0. 07 0. 30 − 2. 39 D is ta nc e to st re am s (m ) B uf fe r (1 00 m ) 26 3, 58 4 23 .0 3 33 60 0. 96 − 0. 65 1. 61 0. 03 0. 05 0. 28 5. 86 B uf fe r (2 00 m ) 20 5, 75 9 17 .9 8 5 9. 09 − 0. 68 0. 10 − 0. 78 0. 2 0. 02 0. 47 − 1. 67 B uf fe r (3 00 m ) 15 9, 80 1 13 .9 7 7 12 .7 3 − 0. 09 0. 01 − 0. 11 0. 14 0. 02 0. 40 − 0. 26 B uf fe r (4 00 m ) 13 1, 42 0 11 .4 9 3 5. 45 − 0. 74 0. 07 − 0. 81 0. 33 0. 02 0. 59 − 1. 37 B uf fe r (> 40 0 m ) 38 3, 71 7 33 .5 3 7 12 .7 3 − 0. 97 0. 27 − 1. 24 0. 14 0. 02 0. 40 − 3. 07 D is ta nc e to ro ad s (m ) B uf fe r (1 00 m ) 13 6, 22 8 11 .9 0 23 41 .8 2 1. 26 − 0. 41 1. 67 0. 04 0. 03 0. 27 6. 11 B uf fe r (2 00 m ) 11 0, 28 3 9. 64 4 7. 27 − 0. 28 0. 03 − 0. 31 0. 25 0. 02 0. 52 − 0. 59 B uf fe r (3 00 m ) 93 ,4 40 8. 17 5 9. 10 0. 11 − 0. 01 0. 12 0. 2 0. 02 0. 47 0. 25 B uf fe r (4 00 m ) 83 ,8 76 7. 33 3 5. 45 − 0. 30 0. 02 − 0. 32 0. 33 0. 02 0. 59 − 0. 53 B uf fe r (5 00 m ) 74 ,6 26 6. 52 3 5. 45 − 0. 18 0. 01 − 0. 19 0. 33 0. 02 0. 59 − 0. 32 B uf fe r (> 50 0 m ) 64 5, 82 8 56 .4 4 17 30 .9 1 − 0. 60 0. 46 − 1. 06 0. 06 0. 03 0. 29 − 3. 64 C T I 0– 4 14 4, 52 9 12 .6 3 50 90 .9 1 1. 97 − 2. 26 4. 24 0. 02 0. 2 0. 47 9. 03 4– 8 98 3, 62 1 85 .9 6 4 7. 27 − 2. 47 1. 89 − 4. 36 0. 25 0. 02 0. 52 − 8. 39 8– 12 16 ,0 77 1. 40 1 1. 82 0. 26 − 0. 00 4 0. 26 1 0. 02 1. 01 0. 26 > 12 54 0. 00 5 0 0 N on e 0. 00 05 0 N on e 0. 02 N on e N on e S P I 0– 20 26 6, 96 2 23 .3 3 15 27 .2 7 0. 16 − 0. 05 0. 21 0. 07 0. 03 0. 30 0. 69 20 – 40 26 7, 92 6 23 .4 2 12 21 .8 2 − 0. 07 0. 02 − 0. 09 0. 08 0. 02 0. 33 − 0. 28 40 – 60 19 1, 32 5 16 .7 2 8 14 .5 5 − 0. 14 0. 03 − 0. 17 0. 13 0. 02 0. 38 − 0. 43 60 – 80 13 0, 68 0 11 .4 2 6 10 .9 1 − 0. 05 0. 00 6 − 0. 05 0. 17 0. 02 0. 43 − 0. 12 80 – 10 0 87 ,7 80 7. 67 4 7. 27 − 0. 05 0. 00 4 − 0. 06 0. 25 0. 02 0. 52 − 0. 11 > 10 0 19 9, 60 8 17 .4 4 10 18 .1 8 0. 04 − 0. 00 9 0. 05 0. 1 0. 02 0. 35 0. 14 S T I 0– 10 27 1, 96 6 23 .7 7 16 29 .0 9 0. 20 − 0. 07 0. 27 0. 06 0. 03 0. 30 0. 92 10 – 20 36 2, 25 5 31 .6 6 17 30 .9 1 − 0. 02 0. 01 − 0. 03 0. 06 0. 03 0. 29 − 0. 12 20 – 30 26 7, 61 9 23 .3 9 12 21 .8 2 − 0. 07 0. 02 − 0. 09 0. 08 0. 02 0. 33 − 0. 27 30 – 40 13 9, 58 2 12 .2 0 5 9. 09 − 0. 29 0. 03 − 0. 33 0. 2 0. 02 0. 47 − 0. 70 40 – 50 58 ,7 32 5. 13 4 7. 27 0. 35 − 0. 02 0. 37 0. 25 0. 02 0. 52 0. 72 > 50 44 ,1 27 3. 85 1 1. 82 − 0. 75 0. 02 − 0. 77 1 0. 02 1. 01 − 0. 77 P la n cu rv at ur e C on ca ve 55 3, 22 7 48 .3 5 21 38 .1 8 − 0. 24 0. 18 − 0. 42 0. 05 0. 03 0. 28 − 1. 50 C on ve x 59 1, 05 4 51 .6 5 34 61 .8 2 0. 18 − 0. 24 0. 42 0. 03 0. 05 0. 28 1. 50 Arab J Geosci (2013) 6:2351–2365 2359 STI and plan curvature. Both weights-of-evidence and certainty factor approaches were applied to analyse the landslide susceptibility using these 12 landslide condi- tioning factors. For each of the conditioning factors, the weights and contrast were calculated using the weights-of-evidence method. The magnitude of the contrast, C, was determined from the difference, W+ and W− that provided a measure of the spatial association between a set of points and a binary pattern (Bonham-Carter 1991). C is positive for a positive spatial association and negative for a negative spatial association. The studentised value of C, the ratio of C to standard deviation or C/S(C), serves as a guide to the significance of the spatial association and acts as a mea- sure of the relative certainty of the posterior probability (Bonham-Carter 1991). The weights and contrasts for each predictor pattern are summarized in Table 2. The contrast was set to the rating of each factor, as the contrast is related to the landslide probability. There were 1,144,281 total pixels in the study area. The ratio (W+) is the percentage of landslides/percentage of the domain and C is the contrast. S2(W+) and S2(W−) are the variances of W+ andW−. S(C) is the standard deviation of the contrast, and C/S (C) is the studentised value of the contrast (Table 2). The standard deviation of C is calculated by SðCÞ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi S2 Wþð Þ þ S2 W�ð Þ p ð5Þ The relationships between the landslides and the landslide-conditioning factors, contrast and studentised C are presented in Table 2. The pixel values obtained are then classified based on natural breaks in Arc GIS 9.3 software into low, moderate, high and very high susceptibility groups to determine the class intervals in the landslide susceptibility map (Fig. 7). Similarly, landslide susceptibility index was calculated using certainty factor model. The landslide distribution for each class, expressed by the number of occurring pixels, was used to calculate CF values. The results of spatial relationship between landslide and con- ditioning factors using certainty factor model is shown in Table 3. In Table 3, slope angle classes showed that 0–5° and 16–30° classes have higher CF weight. As the slope angle increases, the shear stress in the soil or other unconsolidated material generally increases. Gentle slopes are expected to have a low frequency of landslides because of the generally lower shear stresses are associated with low gradients. Steep natural slopes resulting from outcropping bedrock, however, may not be susceptible to shallow land- slides. In the case of slope aspect, most of the landslides occurred in south-east and west facing. This condition may be consequence of humidity in the study area. This may be due to Haraz watershed is affected by pluvial air mass from west and north to north-west. In the case of altitude, both 1,500–1,800 and 1,800–2,100 m classes have 34.54% and 40% of landslide probability and CF values of 0.603 and 0.338, respectively. Results showed that the CF values de- creased with the altitude addition in the study area (Table 3). Investigation of lithological conditions showed that A group consisting of scree, young terraces and old terraces has higher value of CF (0.263). Similarly, group D (PEz and PEf) has lower value of CF (−1). In the case of land use, higher CF value were for residential area (0.834) and mixing orchard and agriculture area (0.633) types of land use. This result referred to anthropogenic (human caused) interferences such as land use change. In the case distance to faults, distances between 0 and 100, 100–200 and 200–300 m have weight (CF) of 0.28, 0.482 and 0.654, respectively. This means that the landslide probability is higher in these intervals. Assessment of distance from streams and roads showed that distance of 0–100 m has high correlation with landslide occurrence. From this obser- vation, we can say that the general trend of the CF value increases with the distance from the streams and roads. So, road construction and bank erosion are most important factors in slope imbalance causing frequent occurrence of landslides. Relation between TWI, and SPI and landslide probability showed that 0–4 and 0–20 classes have highest value of CF, respectively. Similarly, for sediment transport index, class between 40 and 50 has most CF value. The curvature values represent the morphology of the topography. A positive cur- vature is an upwardly convex cell, and a negative curvature is upwardly concave cell. Concave areas generally have a higher CF value than convex areas because slopes with a negative curvature retain more water and for a longer period following heavy rain than slopes with a positive curvature. The curva- ture area, in turn, will increase the moisture content of the soil, Fig. 7 Landslide susceptibility map produced by weights-of-evidence model 2360 Arab J Geosci (2013) 6:2351–2365 Table 3 Spatial relationship between each landslide conditioning factors and landslide by certainty factor model Factor Class No. of pixels in domain Percentage of domain No. of landslide Percentage of landslide CF Value Slope gradient (in degree) 0–5 13,851 1.21 1 1.82 0.334 6–15 64,268 5.62 2 3.64 −0.353 16–30 155,602 13.59 10 18.18 0.252 31–50 343,634 30.03 19 34.55 0.131 51–70 262,117 22.91 10 18.18 −0.206 >70 304,809 26.64 13 23.64 −0.113 Slope aspect North 149,997 13.12 5 9.09 −0.306 Northeast 195,301 17.07 9 16.36 −0.041 East 129,167 11.29 2 3.64 −0.678 Southeast 171,144 14.95 16 29.09 0.486 South 135,677 11.85 3 5.46 −0.54 Southwest 131,718 11.51 9 16.36 0.297 West 79,979 6.99 7 12.73 0.451 Northwest 151,298 13.22 4 7.27 −0.45 Altitude (m) 1,200–1,500 28,463 2.49 0 0 −1 1,500–1,800 157,018 13.72 19 34.54 0.603 1,800–2,100 303,058 26.48 22 40 0.338 2,100–2,400 305,844 26.73 7 12.73 −0.524 2,400–2,700 208,321 18.20 6 10.91 −0.401 2,700–3,000 125,384 10.96 1 1.82 −0.834 >3,000 16,193 1.42 0 0 −1 Lithology A 459,914 40.19 30 54.55 0.263 B 153,621 13.43 3 5.45 −0.594 C 147,386 12.88 2 3.64 −0.718 D 19,655 1.72 0 0 −1 E 363,705 31.78 20 36.36 0.126 Land use Best pasture 246,601 21.55 12 21.82 0.012 Mix orchard and agriculture 152,518 13.33 20 36.36 0.633 Residential 3,450 0.30 1 1.82 0.834 Moderate pasture 741,712 64.82 22 40 −0.383 Distance to faults (m) Buffer (100 m) 44,942 3.93 3 5.45 0.28 Buffer (200 m) 43,132 3.77 4 7.27 0.482 Buffer (300 m) 43,144 3.77 6 10.91 0.654 Buffer (400 m) 44,914 3.92 2 3.64 −0.074 Buffer (>400 m) 968,149 84.61 40 72.73 −0.14 Distance to streams (m) Buffer (100 m) 263,584 23.03 33 60 0.616 Buffer (200 m) 205,759 17.98 5 9.09 −0.494 Buffer (300 m) 159,801 13.97 7 12.73 −0.089 Buffer (400 m) 131,420 11.49 3 5.45 −0.525 Buffer (>400 m) 383,717 33.53 7 12.73 −0.62 Distance to roads (m) Buffer (100 m) 136,228 11.90 23 41.82 0.715 Buffer (200 m) 110,283 9.64 4 7.27 −0.245 Buffer (300 m) 93,440 8.17 5 9.10 0.102 Buffer (400 m) 83,876 7.33 3 5.45 −0.256 Buffer (500 m) 74,626 6.52 3 5.45 −0.164 Buffer (>500 m) 645,828 56.44 17 30.91 −0.452 CTI 0–4 144,529 12.63 50 90.91 0.861 4–8 983,621 85.96 4 7.27 −0.915 Arab J Geosci (2013) 6:2351–2365 2361 which will remain saturated, increase erosion and decrease soil stability (Fig. 8). Landslide susceptibility maps without validation are of little meaningful (Chung and Fabbri 1998). In the literature, three methods of verification analyses have been presented. In the first method, a map produced by GIS is compared to another map prepared by experts using direct observations of the studied area. In the second method, the map obtained is compared with another parameter map that supports the geomorphic process mapped (Lee 2004). In the third method, the GIS-based map is matched with a part of the data set used to produce the GIS-based map. This ap- proach to landslide susceptibility studies has been used by several authors (Remondo et al. 2003; Lee 2005; Ayalew and Yamagishi 2005; Akgun and Bulut 2007; Akgun et al. 2008; Akgun and Turk 2010). In this study, the landslide locations which were not used during the model building process were used to verify the landslide susceptibility maps. The receiver operating characteristics (ROC) curve is a useful method for representing the quality of determin- istic and probabilistic detection and forecasting systems (Swets 1988). The ROC curve is a graphical representa- tion of the trade off between the false-negative and false- positive rates for every possible cutoff value (Table 4). By tradition, the plot shows the false-positive rate (1 specificity) on the x-axis (Eq. 6) and the true-positive rate (the sensitivity or 1—the false-negative rate) on the y-axis (Eq. 7). X ¼ 1� specifity ¼ 1� TN TNþ FP � � ð6Þ Y ¼ sensivity ¼ TP TPþ FN � � ð7Þ The area under the ROC curve (area under curve (AUC)) characterises the quality of a forecast system by describing the system’s ability to anticipate the correct occurrence or non-occurrence of pre-defined ‘events’. The best method Table 3 (continued) Factor Class No. of pixels in domain Percentage of domain No. of landslide Percentage of landslide CF Value 8–12 16,077 1.40 1 1.82 0.227 >12 54 0.005 0 0 −1 SPI 0–20 266,962 23.33 15 27.27 0.145 20–40 267,926 23.42 12 21.82 −0.068 40–60 191,325 16.72 8 14.55 −0.13 60–80 130,680 11.42 6 10.91 −0.045 80–100 87,780 7.67 4 7.27 −0.052 >100 199,608 17.44 10 18.18 0.041 STI 0–10 271,966 23.77 16 29.09 0.183 10–20 362,255 31.66 17 30.91 −0.024 20–30 267,619 23.39 12 21.82 −0.067 30–40 139,582 12.20 5 9.09 −0.255 40–50 58,732 5.13 4 7.27 0.294 >50 44,127 3.85 1 1.82 −0.529 Plan curvature Concave 553,227 48.35 21 38.18 −0.21 Convex 591,054 51.65 34 61.82 0.164 Fig. 8 Landslide susceptibility map produced by certainly factor model 2362 Arab J Geosci (2013) 6:2351–2365 has a curve with the largest AUC; the AUC varies from 0.5 to 1.0. If the model does not predict the occurrence of the landslide any better than chance, the AUC would equal 0.5. An ROC curve of 1 represents perfect prediction. The quan- titative–qualitative relationship between AUC and pre- diction accuracy can be classified as follows: 0.9–1, excellent; 0.8–0.9, very good; 0.7–0.8, good; 0.6–0.7, average; and 0.5–0.6, poor. The ROC curve for the weights-of-evidence and certainty factor models were produced based on the test data set, which was random- ly collected from landslide inventory data (Yesilnacar 2005). The results of the ROC curve test are illustrated in Fig. 9. These curves indicate that, weights-of- evidence model (Fig. 9a) has relatively higher prediction performance than the certainty factor model. ROC plot assessment results showed that in the susceptibility map using weights-of-evidence model, the AUC was 0.7987 and the prediction accuracy was 79.87%. In the suscep- tibility map using CF model, the AUC was 0.7202 and the prediction accuracy was 72.02% (Fig. 9b). Accord- ing to the results of the AUC evaluation, the map produced by weights-of-evidence exhibited satisfactory result for landslide susceptibility mapping. Conclusions In this study, two statistical models such as weight-of- evidence and certainty factor models were used for landslide susceptibility mapping and their performances were com- pared. In both these models, the data acquisition and analysis were relatively easy and not very time consuming. The mod- elling was applied to the Haraz catchments in Iran by consid- ering 12 landslide conditioning factors. In the topographic database, the factors were slope gradient, slope aspect, alti- tude, plan curvature, distance from rivers, distance from roads, TWI, SPI and STI. The lithology and distance from faults was derived from the geological database. The land-use informa- tion was extracted from Landsat ETM+ satellite imagery. An extensive landslide inventory map was produced. For this purpose, a landslide inventory database that is used to assess the landslide susceptibility of the study area, with a total of 78 landslides, was mapped in the study area. The landslide data was randomly spilt into training and testing dataset. Of the 78 landslides identified, randomly 55 (70%) locations were cho- sen for the landslide susceptibility maps, while the remaining 23 (30%) cases were used for the model validation. The ROC curve for the block entry weights-of-evidence was produced based on the test dataset, which was randomly collected from the landslide inventory map. The validation results showed that the weights-of-evidence model has slightly higher predi- cation accuracy, i.e. 7.85% (79.87–72.02%), which is better than the CF model. Here, the authors can conclude that the results of the weights-of-evidence model have shown the best prediction accuracy in landslide susceptibility mapping in the study area. Prepared landslide susceptibility maps could be the basis for decisions making. The information provided by these maps could help citizens, planners and engineers to reduce losses caused by existing and future landslides by means of prevention, mitigation and avoidance. Fig. 9 ROC curve and area under the curve a for the weights-of- evidence model, b certainty factor model Table 4 Parameters for the calculation of ROC curve (modified from Swets (1988)) Landslide bodies Landslide free areas Landslide occurrence based on calculated function True positive (TP) False positive (FP) Safe areas based on calculated function False negative (FN) True negative (TN) Arab J Geosci (2013) 6:2351–2365 2363 Acknowledgements The authors are thankful to anonymous reviewers for their valuable comments which were very useful in bringing the manuscript into its present form. 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Abstract Introduction Study area Weights-of-evidence model Certainty factor model Thematic data preparation Landslide inventory map Landslide conditioning factors Landslide susceptibility maps and their validation Conclusions References