Introduction

Landslide is one of the important natural calamities, which commonly occurred on cut slopes of Ghat roads in mountainous region, while events are also reported in residential areas causing risk to human life, property loss, damage to established road network, and buildings in each year (Aleotti and Chowdhury 1999; Lee and Pradhan 2007; Nandi and Shakoor 2008). Landslides also occur in natural slopes, influenced by various geo-environmental parameters and triggered by rainfall and earthquakes. The natural slopes converted into cut slopes by human intervention for the purpose of transportation network, construction of dams, bridges, and tunnels are more prone to landslide occurrences (Das et al. 2010; Singh et al. 2013a , b). In mountainous terrain, landslides are natural denudation process and its occurrence is subjected to various causative factors and triggered by several external factors such as seismicity, rainfall, water level change, storm waves, and rapid stream erosion (Naranjo et al. 1994; Dai et al. 2002). Hill slopes in mountainous areas are affected by human activities such as building road network, urban development, deforestation, and rapid land use modification, may also influence occurrence of landslides (Das et al. 2011).

The demarcation and mitigation of landslide-prone areas in a region is crucial for future planning and developmental activities. Many Government organizations and several research institutions have spent significant funds to evaluate the landslide hazards and their spatial distribution (Guzzetti et al. 1999). The landslide hazard and susceptibility mapping is an important task for geoscientists, planners and local administrations, as knowledge about the socioeconomic worth of landslides has increased globally (Devoli et al. 2007). Landslide susceptibility refers to the probability of landslide occurrence in a region based on the relationship between causative factors with spatial distribution of landslides (Brabb 1984). Landslide susceptibility mapping describes the type, spatial extent and intensity of past and present landslides in the study area to assess the probable areas for future landsliding, but the frequency or timing is not incorporated (AGS 2007; Anderson and Holcombe 2013). Landslide hazard zonation mapping provides the details about the possible landslide occurrence of a certain type and magnitude in a particular location within a set time (Varnes 1984). Landslide susceptibility maps show the zones, which are stable and possible zones for future landslide occurrence (Corominas and Moya 2008).

The methods of landslide hazard and susceptibility assessment techniques by Mantovani et al. (1996) includes distribution analysis (Wieczorek 1984), qualitative analysis (Kienholz et al. 1984; McKean et al. 1991), statistical analysis (Yin and Yan 1988; Gupta and Joshi 1990; Carrara et al. 1991; Wang and Unwin 1992; Pachauri and Pant 1992; Sarkar et al. 1995; Mark and Ellen 1995; Chung and Fabbri 1999; Lee et al. 2002; Ayalew et al. 2004), deterministic analysis (Skempton and Delory 1957; Okimura 1982; Okimura and Kawatani 1986; Mostyn and Fell 1997; Singh et al. 2013a, b; Ahmad et al. 2013), landslide frequency analysis (Capecchi and Focardi 1988), and distribution-free methods such as fuzzy and ANN. Several studies have been carried out for estimating the spatial probability of landslide occurrences using GIS technique. Many landslide studies were conducted using probabilistic models such as frequency ratio and logistic regression (Atkinson and Massari 1998; Dai et al. 2001; Dai and Lee 2002; Ohlmacher and Davis 2003; Lee et al. 2004; Wang and Sassa 2005; Lee 2005; Lee and Talib 2005; Lee and Pradhan 2006, 2007; Akgul and Bulut 2007; Akgun et al. 2008; Pradhan et al. 2008; Tunusluoglu et al. 2008; Yilmaz 2009a; Yilmaz and Keskin 2009; Ehret et al. 2010; Kannan et al. 2013) and some studies adopted using relative effect model (Ghafoori et al. 2006; Naveen Raj et al. 2011). As a new approach to assess landslide hazards such as fuzzy logic (Ercanoglu and Gokceoglu 2001; Lee 2007; Champati Ray et al. 2007; Kavitha et al. 2008; Kanungo et al. 2009; Pradhan et al. 2009; Srivastava et al. 2010) artificial neural network models were adopted (Lee et al. 2003, 2006; Caniani et al. 2008; Pradhan and Lee 2007, 2010a; Yilmaz 2009b; Poudyal et al. 2010; Chauhan et al. 2010; Choi et al. 2012). In the present study, landslide susceptibility mapping along Ghat road section of Kolli hills was carried out using frequency ratio (FR), fuzzy gamma operator and relative effect (RE) models. The resultant susceptibility maps were compared and prediction accuracies of each map were derived for further evaluation.

Study area

Kolli hills is one of the important tourist spots in Tamil Nadu situated in the tail end of the Eastern Ghats in Namakkal district of Tamil Nadu, India (Fig. 1). The hills cover an area of 283 km2 with an elevation ranges between 1,000 and 1,390 m above mean sea level (MSL). It has mild climate and the location is known for herbal cultivation. Recently, the region of Kolli hills became taluk (administrative boundary) and the total population is 63,888 according to 2001 census covered in 14 villages. The geological assessment in the study area has shown that charnockitic group of rocks, widely present in the north-eastern part of Tamil Nadu State, are well exposed in many prominent hill ranges such as Pallavaram–Chengleput, Javadi, Shevaroy, Chitteri, Kalrayan, Kollimalai (Kolli hills), Pachchaimalai and Nilgiri. In the central part of the State, Kolli hills is situated as a residual plateau-topped charnockitic hill surrounded by pediments and pediplains. Laterite associated with reddish brown ferruginous clayey soil caps on the crystalline rocks at high altitude of Kolli hills derived by the sub-aerial weathering of charnockites and exposed as patches. Irregular lenses and pockets of bauxite and lateritic bauxite are occurring over charnockites at 1,148–1,386 m altitudes above mean sea level (GSI 2006).

Fig. 1
figure 1

Location and digital elevation model (DEM) of Kolli hills Ghat road section along with distribution of past landslide locations

Full size image

The Ghat road with 20 km stretch along 70 hairpin bends connects the foot hills at Karavallikkombai to hill top at Sholaikkadu and is chosen for the present work as study area. The Ghat road falls in between 11°18′29.72″N and 11°19′50.38″N latitudes and 78°19′30.99″E and 78°21′6.49″E longitudes. At present, this Ghat road is the main route to reach the Kolli hills and it aligned in the western slope of Kolli hills. The Ghat road was constructed along cut slopes by modifying the original natural slope condition. Hence, most of the hairpin bends are associated with vertical slope cut rock and soil profiles. These profiles are being exposed to weathering and soil erosion continuously. The major rock types along this Ghat road are charnockites and gneisses, which are mostly fractured and highly weathered. Anbazhagan et al. (2008) have studied the developments of fractures and land subsidence in the south-eastern fringe of Kolli hills due to change of natural slope and drainage condition for cultivation practices indicating frequent slope failure. The structural (discontinuities) and geological (lithology and weathering condition) parameters are the main causative factors, which influence the occurrence of landslides. The topographical factors such as slope gradient, slope aspect, slope curvature and relief are equally important in landslide occurrences. The anthropogenic activities such as deforestation, widening of roads are the common causative factors. Finally, rainfall is the major triggering factor in occurrence of landslides in Kolli hills (Anbazhagan and Ramesh 2014). Landslips, rock fall and debris slides are the common type of mass movements observed in the Ghat road section.

Geospatial database

Identification of suitable landslide causative factors and its mapping need prior knowledge about the major causes of landslides (Guzzetti et al. 1999). Landslide susceptibility mapping needs data collection and construction of a geospatial database to derive landslide-influencing factors. Slope gradient, slope aspect, slope curvature, relief, lithology, land use and land cover, proximity to road, proximity to lineament, and proximity to drainage are the major landslide-influencing factors considered for the landslide susceptibility mapping along Ghat road of Kolli hills.

Landslide inventory

The inventory about the past landslide locations consists of the information about the existing landslides in an area, which is helpful to validate the landslide susceptibility map. An understanding of conditions and processes which influences past landslide occurrences and their evidences is the most significant aspect in the prediction of future landslides (Yilmaz et al. 2012). In the present study, landslide inventory mapping has been carried out by collection of existing historical reports and photographs from highways department, newspaper records, and enquiry made from local people. Through data collection, it was inferred that about 74 major and minor landslide events with different intensity occurred at 54 locations. These 54 past landslide locations were collected and plotted as point feature along Ghat road section digitized from high-resolution satellite image. In the present case, it is difficult to represent the landslide areal extent in the form of polygon feature, because the landslide areas are too small to highlight in the current scale (Yilmaz 2009a). Hence, the inventory locations are entered as point feature. The inventory map depicts that occurrence of landslides was distributed all along the Ghat road (Fig. 1). Debris slide and rock fall are the most common types of landslides along the Ghat road (Fig. 2).

Fig. 2
figure 2

Field photographs show past landslide occurrences

Full size image

Factors derived from digital elevation model

Surface topography decides the flow sources and controls the run-off direction, which limits the density and spatial extent of landslides (Sujatha et al. 2012). A digital elevation model (DEM) with 10 m resolution was generated from 20 m interval contours digitized from 1:50,000 scale topographic map. From DEM, the slope gradient, slope aspect, slope curvature and relief were derived using the Spatial Analyst tool of ArcMap 9.1 © ESRI.

Slope gradient

Slope gradient is an important factor in assessment of slope stability, which measures the rate of change of elevation in the direction of steepest fall and gravity supports the flow of water and other material in the direction of slope. It is having great importance in hydrology and geomorphology, which disturbs the speed of surface and subsurface flows (Anbalagan 1992; Ercanoglu and Gokceoglu 2004; Kannan et al. 2013). The slope gradient map (Fig. 3a) was reclassified into five classes viz., <13°, 13–22°, 22–32°, 32–45°, and >45º, following the Jenks natural breaks classification method (Jenks 1967).

Fig. 3
figure 3

Landslide causative factors; a slope gradient, b slope aspect, c slope curvature, d relief, e lithology, f land use and land cover, g road buffer, h proximity to drainage, i proximity to lineament

Full size image

Slope aspect

Slope aspect describes the direction of slope. Aspect shows a strong inheritance from bedrock structure and influences the exposition to sun and thereby changes in vegetation and evapotranspiration (Fernandez Merodo et al. 2004). The slope aspect (Fig. 3b) is divided into eight directional classes as N (337.5°–22.5°), NE (22.5°–67.5°), E (67.5°–112.5°), SE (67.5°–157.5°), S (157.5°–202.5°), SW (202.5°–247.5°), W (247.5°–292.5°), and NW (292.5°–337.5°). Apart from that few pixels are fall in Flat (−1) region. Slope aspect in eight directional classes is generally adopted by many workers (Sarkar and Kanungo 2004; Kanungo et al. 2006; Pradhan and Lee 2010a). In the present study, most of the landslide locations fall in NW and W slope directions.

Slope curvature

In general, curvature is defined as the curvature of a line formed by intersection of a random plane with the surface (Duman et al. 2006; Ramani et al. 2011). In the present study, the combo curvature which is the combination of plane and profile curvature is taken into consideration. The characterization of slope morphology and flow can be analyzed with the help of combo curvature map (Catani et al. 2013). In the case of curvature map, the negative values were classified as concave, the positive values were classified as convex, and zero values classified as flat (Fig. 3c). The major part of Ghat road section is covered by convex slope, followed by concave and flat condition. The maximum numbers of (24) past landslides were located in convex slope, and then 18 landslide locations were fall in flat slope.

Relief

Elevation is useful to understand the local relief and determines the maximum and minimum heights within the study area. In the present study area, the relief varies from 280 to 1,220 m. On the basis of Jenks natural breaks classification (Jenks 1967), the relief range was classified into five classes viz., 280–426, 426–618, 618–841, 841–1,040, and 1,040–1,220 m (Fig. 3d).

Geological parameters

The lithology map of the study area is prepared from a 1:50,000 scale geological map published by GSI (1995). The charnockite and hornblende–biotite gneiss are the major lithological units shown in Fig. 3e. The land use and land cover were interpreted from IRS-P6 LISS IV satellite image with 5.8 m spatial resolution acquired in the year 2006. The remotely interpreted details were verified in the field. The presence of vegetation increases erosion resistance while barren slopes are susceptible to landslides (Dai and Lee 2002). Fairly dense forest, dense forest, forest plantation, and settlement are the various land use and land cover units recognized (Fig. 3f). Most of the road section is covered by structural hill system.

Proximity analysis

Modification of natural slope as cut slope in road construction is the most significant anthropogenic activity which causes slope instability. Road sector acts as a barrier or a path for water flow, depending on its location in the basin, may trigger occurrence of landslide (Pradhan and Lee 2010b). Since, most of the landslides were located at short distance from the road, a 5 m buffer zone was generated and considered in the present study (Fig. 3g).

The drainage map of the study area was prepared with the help of Survey of India topographical map on 1:50,000 scale. The influence of drainage on occurrence of landslide may vary in space depending upon the distance of the source area from the original stream flow (Srivastava et al. 2010). Numerous local planes of weakness were interpreted from high-resolution satellite imagery as lineaments. The occurrences of landslides are expected more in zones that fall close to these planes and less away from them. The distance from drainage and lineament was calculated using euclidean distance method in ArcGIS 9.1, and reclassified into five classes (Fig. 3h, i), based on Jenks natural breaks classification (Jenks 1967).

Methods

Frequency ratio (FR) model

Frequency ratio approach is based on the observed relationships between landslide distribution and each landslide-influencing factor. The relationship provides the correlation between landslide occurrence location and the factors influencing landslide in the study area. The frequency ratio of each class in a thematic layer was calculated from their relationship with past landslide events. In the relationship analysis, the ratio is that of the area where landslides occurred to the total area, so that a value of 1 is an average value. If the value is greater than 1, it means a higher correlation, and a value lower than 1 means lower correlation. The frequency ratios of each thematic layer were then summed to estimate the landslide susceptibility index (LSI) (Eq. 4) (Lee and Talib 2005; Lee and Pradhan 2007).

$${\text{Frequency ratio}} = \frac{\text{Slide ratio}}{\text{Class ratio}}$$
(1)

where,

$${\text{Slide }} {\text{ ratio}} = \frac{{{\text{Number }} {\text{ of }} {\text{ landslide }} {\text{ grids }} {\text{ in }} {\text{ class}}}}{{{\text{Total }} {\text{ number }} {\text{ of }} {\text{ landslide }} {\text{ grids}}}}$$
(2)
$${\text{Class }} {\text{ ratio}} = \frac{{{\text{Number }} {\text{ of }} {\text{ grids }} {\text{ in }} {\text{ individual }} {\text{ class}}}}{{{\text{Total }} {\text{ number }} {\text{ of }} {\text{ grids }} {\text{ in }} {\text{ whole }} {\text{ class}}}}$$
(3)
$${\text{LSI}} = \varSigma {\text{FR}}$$
(4)

(LSI landslide susceptibility index; FR frequency ratios of each influencing factor)

The landslide susceptibility value represents the relative susceptibility to landslide occurrence. So the greater the frequency ratio value, the higher the susceptibility to landslide occurrence and the lower the value, the lower the susceptibility to landslide occurrence.

Relative effect (RE) model

The relative effect model is based on the observed logarithmic relationships between distribution of landslides and each landslides-related factor. The main advantage of logarithmic function is in domain determination for output data which provides equal opportunity for plus and minus domains of calculated relative effect values (Ghafoori et al. 2006; Naveen Raj et al. 2011). Using the relative effect model (Eq. 5), the spatial relationships between landslide occurrence location and factors contributing to landslide occurrence were derived for each class in a landslide-influencing factor map.

$${\text{RE}} = {\text{Log}} \left( {\frac{\text{SR}}{\text{AR}} + \varepsilon } \right)$$
(5)
$${\text{AR}} = \frac{a}{A}$$
(6)
$${\text{SR}} = \frac{\text{sld}}{\text{SLD}}$$
(7)

where RE is the relative effect, a is the total number of grids in individual class, A is the total number of grids in the study area, sld is the total number of landslide grids in the individual class, SLD is the total number of landslide grids in the study area and ε is a very small positive value near zero

$${\text{LSI}} = \mathop \sum \limits^{{}} {\text{RE}}$$
(8)

In the present relationship analysis, there are following three cases for estimating relative effect of each class, based on its RE.

Negative effect—when the slide ratio is less than its class ratio, the RE value is less than zero. The case indicates that, it has an effect of decreasing landslide susceptibility.

Positive effect—when the slide ratio is greater than its class ratio, the RE value is greater than zero. The case indicates that, it has an effect of increasing landslide susceptibility.

Zero effect—when the slide ratio is equal to class ratio, the RE value is zero. This means that it has no effect of landslide susceptibility.

Fuzzy set theory

Fuzzy set theory was first suggested by Zadeh (1965), which differs from the conventional Boolean set theory in such a manner objects within a set is definite. In the case of conventional set, the membership is 1 if the object is completely present in the set; if not in the set then the membership is 0. In fuzzy set, the extent of membership values of an object can have values ranges between 0 and 1, instead of having complete membership 0 or 1 as in a conventional set. The assignment of membership values between 0 and 1 is an important part in the fuzzy logic analysis. There is no regulation to assign the fuzzy membership values. The only thing is that the selected values must imitate the extent of membership of a set. It can be user defined based on subjective investigation (Bonham-Carter 1994) or the values can be derived from statistical analysis like information value (Yin and Yan 1988) and from frequency ratio (Lee 2007) or it can be assigned by various functions representing the reality such as J-shaped, sigmoidal and linear (Eastman 1995) or using analytical hierarchical method (Saaty 1978). With respect to a given data set (a thematic map), a membership can be assigned to each map category according to its direct/indirect relationship with the phenomena in consideration (Champati ray et al. 2007). The purpose of the integration of data is to combine various parameters collectively to explain and investigate relations to make conclusion about the problem under consideration. The method of integration of data may be based on complete statistical/quantitative approach, may be absolute knowledge based/qualitative method, where the lack or non-existence of sufficient data and may be the combination of both statistical as well as knowledge-driven method (Srivastava et al. 2010). A variety of operators can be employed to combine the membership values for the given two or more thematic maps with fuzzy membership functions for the same set. Bonham-Carter (1994) discussed five operators, viz., the fuzzy AND, fuzzy OR, fuzzy algebraic product, fuzzy algebraic sum, and fuzzy gamma operator. This study uses the fuzzy gamma operator for combining the fuzzy membership values.

The fuzzy algebraic product is defined as:

$$\mu {\text{combination}} = \mathop {\mathop \prod \limits^{n} }\limits_{i = 1} {\mkern 1mu} \mu i$$
(9)

The fuzzy algebraic sum is defined as:

$$\mu {\text{combination}} = 1 - \mathop {\mathop \prod \limits^{n} }\limits_{i = 1} {\mkern 1mu} (1 - \mu i)$$
(10)

where, \(\mu i\) is the fuzzy membership function for the ‘i’ th map, and i = 1, 2, 3,…, n maps to be combined.

$$\mu {\text{combination}} = ({\text{FAS}})^{\gamma } \times ({\text{FAP}})^{1 - \gamma }$$
(11)

where, γ is a value chosen in the range 0–1; FAS-fuzzy algebraic sum; FAP-fuzzy algebraic product.

In the present study, the membership values for each thematic layer were derived by normalizing the probability frequency ratio values. However, the zero membership values have been assigned as 0.0001 to avoid the complete slackness of a class in a thematic map landslide hazard zonation analysis (Srivastava et al. 2010).

Results and discussion

The frequency ratio (FR) and relative effect (RE) values were calculated using Eq. 1 and Eq. 5 and fuzzy membership values were normalized from frequency ratio values for all the classes present in entire parameter maps based on the relationship with past landslide locations (Table 1).

Table 1 Frequency ratio, relative effect and fuzzy membership values for landslide causative factors
Full size table

Landslide susceptibility zonation

Landslide susceptibility analysis has been carried out through various methods. In the present study, the frequency ratio, fuzzy gamma operator, and relative effect models were adopted. For the application of frequency ratio, fuzzy gamma operator, and relative effect model, all the nine landslide causative factors were converted to a raster format with 10 × 10 m size grids to calculate the landslide susceptibility index (LSI). The total number of study area grids was 18,645, and the total number of landslide grids in the study area was 54. All the influencing factors were taken to the spatial analysis extension of the ArcGIS software for integration. The LSI was calculated based on the integration rules as shown in Eqs. 4, 8, and 11. The integration was carried out using the raster calculator option of the ArcGIS software. If the LSI value is high, it means a higher susceptibility to landslide; a lower value means a lower susceptibility to landslides. For frequency ratio model, the minimum, mean, maximum and standard deviation of LSI are 2.590000153, 9.679908202, 23.31999969, and 3.924949664, respectively. For relative effect model, LSI values had a minimum value of −4.25, mean value of −0.614627511 and a maximum value of 2.769999981, with a standard deviation of 1.258164037. In the case of fuzzy gamma operator, the value of γ was set to 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.975, and 0.98 to observe its effect on the landslide susceptibility map. The integrated result with the gamma value (γ = 0.975) was produced showing highest accuracy than other values tested in the present study. In the case of applying the gamma operator (γ = 0.975), the minimum, mean, maximum, and standard deviation values of LSI are 0.070383042, 0.530481696, 1, and 0.150946113, respectively.

The landslide susceptibility maps (Fig. 4a, b, c) were classified into five susceptibility classes based on Jenks natural breaks classification method viz., very low, low, moderate, high and very high. The 54 known landslide locations were overlaid on the landslide susceptibility maps produced by frequency ratio, relative effect, and fuzzy gamma operator models, and the percentage of pixels of known landslides under various susceptibility classes was calculated. The frequency ratio model identified 92.59 % of the known landslides were concentrated in the high and very high susceptibility classes, whereas relative effect model predicted 94.44 % of the known landslides in the high and very high susceptibility classes. The fuzzy gamma operator model (γ = 0.975) possesses 96.30 % of the known landslides in the high and very high susceptibility classes. The percentage of landslide occurrence in each susceptibility class for all the three models is shown in Table 2 and as a bar diagram in Fig. 5.

Fig. 4
figure 4

Landslide susceptibility maps. a Based on frequency ratio model, b based on fuzzy gamma operator (using γ = 0.975), and c based on relative effect model

Full size image
Table 2 Percentage of known landslides falling into the different landslide susceptibility classes, determined by the frequency ratio, fuzzy gamma operator and relative effect models
Full size table
Fig. 5
figure 5

Relationship between landslide frequencies with susceptibility classes

Full size image

Comparison and validation of the models

The Area Under Curve (AUC) is one of the most commonly used qualitative accuracy assessment method for the prediction models in natural hazard assessment (Begueria 2006; Pradhan and Lee 2009). The success rate describes how well the model and factor predict the landslide (Chung and Fabbri 1999). The accuracy of frequency ratio, fuzzy gamma operator, relative effect models in landslide susceptibility mapping in the Kolli hills was evaluated by calculating “area under the curve (AUC)”. The validation of landslide susceptibility analysis result was achieved by comparing the known landslide location data with the landslide susceptibility maps. The success rate curves were produced and the “area under the curve (AUC)” was calculated for all the three susceptibility maps using the existing landslide location data. To obtain a relative landslide susceptibility index ranks for each prediction model, the calculated index values of all grids in the study area were sorted in descending order. Then the ordered cell values were divided into 100 classes, with accumulated 1 % intervals. An ideal model shows an AUC value close to 1.0, whereas a value close to 0.5 indicates inaccuracy in the model (Fawcett 2006). The AUC value of success rate curve for relative effect model was found to be 0.8526. The AUC value for landslide susceptibility map produced by fuzzy gamma operator (using γ = 0.975) was calculated as 0.8733. The AUC value for frequency ratio model showed highest accuracy of 0.8793. The comparison result of success rate curve is shown as a line graph in Fig. 6. These results point out that frequency ratio model shows good prediction pattern than fuzzy gamma operator and relative effect model of landslide susceptibility in the study area. Moreover, the success rate curve for frequency ratio model had a greater steepness in the first part of the curve, signifying its greater predictive capability (Nandi and Shakoor 2009).

Fig. 6
figure 6

Illustration of success rate curve showing landslide susceptibility index rank (x-axis) occurring in cumulative percent of landslide occurrence (y-axis)

Full size image

Conclusions

In the present study, frequency ratio, fuzzy gamma operator, and relative effect models were adopted for the landslide susceptibility mapping along Ghat road section of Kolli hills, Tamil Nadu. Landslide susceptibility maps have been produced using the relationship between each landslide-influencing parameters and known landslide locations. The results have shown that the occurrence of landslides were more prominent along cut slope, slope gradient in 32°–45°, convex curvature, and relief in 618–1,040 m categories. The validation results show that the frequency ratio model has better prediction accuracy (AUC = 0.8793) than the fuzzy gamma operator model (AUC = 0.8733) and relative effect model (AUC = 0.8526). The landslide susceptibility maps are the source for decision making and developmental activities in an area. Hence, the output results of the present study can help the developers, planners, and engineers for slope management and land-use planning in the study area.