The Effect of Aspect on Landslide and Its Relationship with Other Parameters

*Seda Cellek*

#### **Abstract**

Aspect is one of the parameters used in the preparation of landslide susceptibility maps. The procedure of this easily accessible and conclusive parameter is still a matter of debate in the literature. Each landslide area has its own morphological structure, so it is not possible to make a generalization for the aspect. In other words, there is no aspect in which landslides develop in particular. Generally, landslides occur in areas facing more than one direction. The biggest reason for this is that those areas are under the influence of other parameters. Therefore, it is wrong to evaluate the aspect, alone. Since it is a part of the system, it should be evaluated together with other conditioning factors. In this research, many landslides susceptibility studies have been investigated. The directions and causes of landslides have been determined from the studies. In addition, the criteria of the used aspect classes have been investigated. In the literature, the number of class intervals chosen, and their reasons were investigated, and the effects of this parameter were tried to be revealed in new sensitivity studies.

**Keywords:** Landslide, susceptibility, aspect, parameter, classification

#### **1. Introduction**

There are many different definitions of aspect in the literature. These definitions are made in three ways: by direction, by maximum variation, and by degree. The first definition group is the most commonly used. The concept of direction is to the come to the forefront. According to some researchers, the aspect at a point on the land surface is the direction that the tangent plane passing through that point faces and is expressed in degrees (the angle defined in the clockwise direction from the north) [1]. In its simplest form, the aspect is a data type that expresses the geographical direction in which the slopes develop.

According to the second definition, the aspect represents the maximum slope direction of the land surface [2]. Or, for any point, the aspect represents the direction of the maximum variation of the degree of variation of the height value [3]. According to some researchers, it is defined as the compass direction of the maximum rate of change [4, 5]. According to some researchers, it can also be defined as the slope direction, which defines the downward direction of the maximum rate of change in maximum, or as the dip direction, which defines the downward slope direction of the maximum altitude change rate [6, 7].

#### *Landslides*

According to the third and last definition, the expression of the directions in degrees is in the foreground. Aspect defined it as the clockwise faces of a slope varying between 00 and 3600 , measured in degrees from the north [8, 9]. Generally, the aspect ranges from 0° to 360° and are handled as 45° groups, and the directions are grouped clockwise as north, northeast, east, southeast, south, southwest, west and northwest.

An aspect map shows both the direction and grade of a terrain at the same time. Therefore, it is an important factor in the analysis and production of landslide susceptibility maps. In the literature, there are many studies that accept and use aspect, landslide, as the main conditioning factor [2–12]. While some authors [13–16] consider landslides as a controlling factor, others [17, 18] do not see it as a conditioning factor. While some researchers say that aspect has no significant effect on landslides [19], some researchers have also argued that there is an important relationship between slope aspect and landslide occurrence [20]. According to most researchers, aspect has an indirect effect on landslide [21]. While some researchers associate this relationship mainly with precipitation [22–31], others have associated this with the general morphological trend of the area [27, 32]. According to most researchers, it has been argued that the relationship between landslide and aspect is also related to the dominant wind direction [33–35]. Some researchers, on the other hand, consider the effect of the aspect on the landslide, the general precipitation direction of the region, freeze–thaw, sunlight [35], longer snow retention on sundrenched slopes, moisture retention, soil type, permeability, porosity, moisture, organic components, land and vegetation (forest, grassland, bushland, farmland), evapotranspiration [36], evaporation transpiration, climatic season, rock structure [37], It explains that factors such as discontinuities and fault orientation decrease the slope stability [10, 11, 24, 28, 30, 32, 38, 39]. Many parameters are used in landslide susceptibility studies, but it is stated that there are very few parameters that are thought to have a direct effect on landslides. The aspect parameter has also been investigated for a long time [3, 16, 28, 40–43], but it is one of the parameters on which no consensus can be reached [3, 44–47]. In the examined studies, it was determined that the aspect parameter indirectly affects the landslide. It is thought that this parameter triggers the landslide together with other parameters. Some researchers, especially in their studies on small-scale landslides, have determined that the angle with the slope affects the stability negatively [48–50]. Many researchers state that aspect is as effective as slope in the formation of landslides [11–13, 23, 24, 28, 30, 45, 51–55]. Apart from slope, aspect is one of the most important parameters in preparing hazard and zoning maps [13, 23, 24, 28, 30, 54].

As seen from the studies examined, the aspect parameter is a parameter that differs in each study area. For this reason, it has been interpreted that it should be examined together with other parameters rather than being an effective parameter in terms of landslide susceptibility alone [46]. According to Ramakrishnan et al. [56] stated in their study that different types of mass movements (plane, wedge, slope and soil slide) play an important role in control. However, there is no determination as to the extent to which the bee affects the landslide susceptibility.

In studies, landslides must be concentrated on slopes with a certain orientation in order to take into account the aspect. In many studies, researchers have determined that landslides are concentrated on slopes with certain orientations in their statistical evaluations [13, 22–30, 51, 57]. However, there are studies using the parameter in studies conducted in areas with equal landslide distribution in all directions. Generally, in such a finding, the lowest score is given to the aspect parameter.

The aspect factor is controlled by the climate process. Elevation and slope angle are also effective factors on this parameter. On the other hand, there are processes

#### *The Effect of Aspect on Landslide and Its Relationship with Other Parameters DOI: http://dx.doi.org/10.5772/intechopen.99389*

controlled by the aspect factor. The most important of these is plant ecology. This is followed by forestry, site selection and planning. Land morphology is under the influence of structural elements. It takes a long time to change. The biggest factor controlling the view is the structural and dynamic morphological conditions that form the silhouette of the field from past to present [58].

Although there are parameters that are agreed upon among researchers in the literature, look is not among them. For this reason, with this study, the relationship between aspect and landslide was tried to be revealed and this uncertainty in the literature was examined.

#### **2. Effect on other parameters and landslide**

It is stated that the parameter contributes to the landslides by affecting other parameters. Since wind direction causes precipitation intensity and erosion of sun-facing slopes, aspect indirectly affects landslide [33, 34]. Although it is stated in the literature that the effect increases with the angle of slope and elevation, the effect on landslides is mostly mentioned together with the climatic conditions. Aspect parameter is generally in close relationship with climatic conditions [59]. The parameter determines the effect of rain direction, amount of sunlight, solar heat, soil moisture, wind and air dryness [39, 60]. Since it controls the soil moisture concentration with the effect of climate, it is considered as an important factor indirectly triggering landslides [61–63]. Therefore, due to its morphology, how the aspect factor affects the climatic parameters by modifying it should be correlated.

The conditions for the slopes facing different directions to be affected by atmospheric events such as precipitation, sun, light, freeze–thaw are also different. Therefore, it is possible to evaluate the relationship of the parameter with the climate in 3 parts. These are precipitation, sun and wind.

#### **2.1 Aspect-precipitation relationship**

The most important factor affecting aspect is precipitation. Most of the researchers studying the aspect parameter associated landslide with precipitation. In the literature, there are studies that argue that slopes that receive precipitation and are in the shade are more susceptible to landslides. In the literature, there are researchers who stated that landslides are very common on the slopes where monsoon precipitation falls more frequently in the study areas [2, 35, 64–66]. After exposure to physical weathering during the dry season, they are prone to landslides with the emergence of strong monsoon precipitation and winds [67]. In their study in Greece, Alexakis et al. [68] and Kouli et al. [69] determined that the slopes facing northeast and northwest received heavy rainfall and the most landslides were observed here.

If precipitation exceeds the threshold value in an area and the area is unstable, landslides are likely to occur. In this respect, precipitation should be considered as a triggering factor and aspect as a preparatory factor. Critical slope angle values of soils in dry and saturated conditions are examined. It has been determined that the saturation or dryness of the soil affects the critical slope angle by about 40%. In this case, the slopes receiving the most precipitation were considered the most dangerous, and the slopes receiving the least precipitation were considered the least dangerous [27, 70].

The reason for the fact that landslides are significantly higher on a slope facing any direction compared to the others is that the torrential rains and heavy rains that developed during the landslide occurred along a line from that direction.

For this reason, it can be observed that landslides are more intense on slopes that receive heavy rainfall. This depends on the infiltration capacity, which is controlled by many factors such as the type of soil, its permeability, porosity, moisture and organic matter content, vegetation and the season in which precipitation occurs. Slopes that receive precipitation reach saturation more quickly and cause higher pore water pressure to develop within the soil. As a result, the pore water pressure on these slopes increases [11, 42, 67, 71, 72].

#### **2.2 Aspect-snow water/freeze: thaw relationship**

It has been determined that there is a negative effect on the landslide mechanism in the form of the reason that the snow cover stays longer in the places that are not exposed to the sun and the water holding capacity increases accordingly [20, 73–76]. Avcı [76] determined that in the Esence Stream Basin, which is the study area, the south-facing slopes receive plenty of precipitation with the effect of the facade systems, this precipitation falls in the form of snow in the winter season, and the increase in the amount of snow melts and precipitation in the spring season facilitates the landslides.

Landslides occurring in a certain slope direction are associated with long-term freezing and thawing movements [20, 73, 77]. In certain directions it is associated with increased snow concentrations and thus longer times for freeze and thaw action and intense erosion [77].

#### **2.3 Aspect- solar radiation and wind relationship**

Calligaris et al. [78] defined the aspect as the reflection of the sun's insolation. Aspect affects solar radiation and therefore temperature. Aspect affects the amount of heat energy taken from the sun and thus water loss by transpiration and evaporation [79]. The slopes that are most exposed to the sun's rays reveal evapotranferance [9]. This affects the soil moisture in the ground. In addition, evaporation affects vegetation distribution and type. In the literature, there are researchers who determined that landslides occur more intensely on slopes that are more exposed to sunlight [9, 11, 35, 39, 42, 71, 72, 80, 81]. In the literature, there are studies that determine that slopes that receive sun are more prone to landslides than slopes that receive rain. Bijukchhen et al. [82] determined that in their study areas, in general, slopes sloping towards the sunlight and precipitation region have a higher landslide hazard propensity compared to the slope in the rain shadow. Although this parameter is usually evaluated together with the aspect, Görüm [83] determined in her literature research that 72 studies used aspect and 3 studies used sun exposure as an input parameter.

Remondo et al. [84], on the other hand, used the values on this date in their studies for landslide susceptibility assessment, since 21 March will be the most sun exposure. Tasoglu et al. [85], in their work; they determined that it was exposed to direct sunlight in east, southeast, south and southwest directions and sunlight was quite effective in inducing landslides.

Like exposure to sunlight, the drying wind also controls soil moisture concentration. This is a determinant of landslide occurrence [61, 62, 67, 71]. Slope exposure shows possible effects of prevailing winds, differential weather and related effects.

#### **2.4 Aspect-geology relationship**

Lithology: indirectly, it triggers the landslide together with the view. Afungang et al. [86] determined that thick pyrolastics as debris in the study areas were more

#### *The Effect of Aspect on Landslide and Its Relationship with Other Parameters DOI: http://dx.doi.org/10.5772/intechopen.99389*

susceptible to landslides in windward slope directions. Yeşiloğlu [87] evaluated the effects of lithology and landslide together in his study. An aspect map has been created to be used in the evaluation of the relationship between the production of debris material from limestones and aspect. According to Ayalew et al. [70] stated in their study that the distribution of landslides in regions close to the oceans increases with the effect of wave effect, weathering and subsequent coastal erosion.

Along with the fault, there are also those who research the effect of the landslide on the landslide, there are also those who research the effect of the landslide on the landslide. There are researchers who observed that landslides intensified in certain slope directions before and after the earthquake in the study areas [2, 39, 88, 89].

Guillard and Zezere [90] stated that south-facing slopes receive more sunlight than north-facing slopes in their study area, but since the geological structure of the area is characterized by a monocline dipping to the south and southeast, more landslides occur on south-facing slopes.

#### **2.5 Aspect- vegetation cover relationship**

Aspect plays an important role in stability assessment; because it controls vegetation distribution, type, density and root growth on a land [11, 39, 80, 91]. It also controls moisture content in soil and vegetation growth due to exposure to sunlight, which also affects soil strength, landslide, infiltration and run-off rates [63, 92]. Dahal [93] added aspect data in his research for the purpose of detecting plant propagation and increasing the accuracy rate according to the aspect effect in the study area.

Champati ray et al. [94] and Srivastava et al. [95] found that most of the southfacing slopes in the Himalayan study areas were devoid of or have insufficient vegetation due to low soil moisture, which plays an important role in the assessment of slope stability in their field. On the other hand, the north side is less exposed to the sun's rays, thus conserving the moisture in the soil. For this reason, taller trees are growing, which tends to stabilize the northern slope. The absence of vegetation provides the slope material with dryness and therefore reduces its adhesion strength.

#### **3. Relation of aspects to each other**

During the literature review, it was determined that while more intense landslides were observed on the slopes facing one direction, less landslides were observed on the opposite side of this direction. Since the "south, southeast, southwest and west" aspects are generally warmer in Turkey, they are called sunny aspects. On the contrary, "north, northeast, northwest and east" aspects are also called shaded aspects because they are cooler. The sun exposure times of these two groups differ markedly. Since the slopes facing south and west are more exposed to sunlight, evaporation is rapid in these regions. Otherwise, since evaporation is slow and the soil stays moist for a long time, the risk of flooding is higher on north and east facing slopes in case of excessive precipitation [96]. Again, in his field study in Turkey, Ozsahin [97] determined the probability of the highest landslide occurrence as N and W directions and stated that the humidity was relatively higher on the slopes facing these directions.

#### **3.1 South (S)**

In areas where landslides occur on the south side, a higher amount of solar insulation occurs. On slopes with higher insulation and higher temperatures,

erosion increases. Areas where vegetation is removed are exposed to direct sunlight, creating drier soil conditions, which increases the likelihood of landslides [98]. According to Devkota et al. [47], Hong et al. [99] and Chena et al. [11], most of the landslides occurred on the slopes facing south and southeast in the study areas. The biggest reason for this is that the highest precipitation rate is seen on the southfacing slopes. Meinhardt et al. [65] determined that the water saturation of the slopes increased with the effect of southwest monsoon rains in the study areas and the highest slip density was found in the south and southwest. Tombus [100], on the other hand, determined in his study that the erosion value is higher on slopes facing south than on slopes facing other directions.

#### **3.2 North (N)**

In the studies conducted in the Black Sea, it was observed that landslides were intense on the slopes facing north. The reason is that the region is under the influence of precipitation from the north and north-facing slopes are more affected by precipitation. From this, it can be concluded that the air currents coming from the sea in the study areas close to the sea will affect more areas in the region. It is known that the Black Sea receives more precipitation than the north due to the high evaporation of precipitation. For this reason, north-facing slopes are examined as the most dangerous in terms of soil saturation in the study area, and south-facing slopes are examined as the least dangerous. [101, 102]. According to Hadji et al. [9] determined that the slopes in the study area are mostly in the north-facing directions. In addition, they determined that the most precipitation in winter comes from the northwest. They also determined that they affect the clays in the ground and therefore trigger landslides.

#### **3.3 South (S)-North (N)**

In their study, Lineback et al. [103] found more landslides in the north and northwest-facing directions than in the south-facing directions. They stated that the southern parts remained drier as the reason for this. Wang and Unwin [104], on the other hand, found evidence in their study that the probability of slipping increases in the north-facing slope direction. As justification, they showed that the main precipitation directions in the Zagros Mountain Belt are north and west, and the main solar direction is east and south [105]. According to Saha et al. [4] determined that, in general, south-sloping slopes have less vegetation density than north-facing slopes, and therefore they are more sensitive to landslide activity in the study areas. On the other hand, Marston et al. [106] observed that, due to geographical conditions, north and west facing slopes have a higher moisture content for a longer period of time and cause higher landslide susceptibility in their study area. They emphasize that exposed soil on south-facing slopes is subject to cycles of wetting and drying, thereby increasing landslide activity in the Himalayas [20]. According to Rahman et al. [79] found that south-facing slopes were more exposed to the sun and north-facing slopes were least exposed to the sun in their study area.

As a result, they determined that the north direction and the least south direction were sensitive to landslides in their fields. They showed that the reason for this is that it takes longer time for the soil to dry in the shaded areas on rainy days. According to Akinci et al. [107] found that in the study areas, the slopes are more north-oriented and again, landslides occur mostly in this direction. They stated that these slopes are more humid with the effect of aspect, while the temperature and evaporation are low on the slopes facing north, and the soil moisture is high.

#### *The Effect of Aspect on Landslide and Its Relationship with Other Parameters DOI: http://dx.doi.org/10.5772/intechopen.99389*

In addition, they stated that the amount of precipitation and snow melts are high on the southern slopes. Afungang et al. [86] found that north and northwest-facing slopes at higher altitudes received more precipitation and sun than south-facing slopes. Therefore, it was determined that the southwest-facing slopes were drier, less windy, and received less solar radiation with less landslides. Champati ray et al. [94] and Srivastava et al. [95], in their study in Himelaya, found that more landslides occurred on the southern front compared to the northern front. Temiz [101] and Yalçın [102], on the other hand, determined that north-facing slopes were the most dangerous in terms of soil saturation in the study area, and south-facing slopes were the least dangerous.

#### **4. Aspect classes**

The reasons for the change in the number of class intervals can be counted as the slopes being oriented in a certain direction, the absence of landslides in some directions or the presence of very few pixels. It is usually given to flat areas such as lakes and seas [20]. For example, the probability of landslides in "flat" areas is almost zero [34]. However, Yeşilnacar and Topal [108] with Çevik and Topal [109] stated that the landslides in the study area occurred equally in different slope orientations and emphasized that it is not an effective parameter in their studies. Aspect is measured clockwise towards north and takes positive values between 0 and 360 degrees. Aspect is measured clockwise towards north and takes positive values between 0 and 360 degrees. In order to create a slope orientation map, on the basis of 4 main geographical directions and these main directions (NE, NW, SE and SW), which of these directions the slopes face in the study area and their relations with the directions of the landslides are determined [101, 102]. It indicates 0° north, 90° east, 180° south and 270° west [32]. In the landslide analysis, a categorical structure is formed according to 450 angles. When the researchers grouped the slope orientation values in their studies, they determined which orientations the landslides intensified. The perspective angles and values made in the studies are given in **Table 1**.

In studies, very different grade ranges from 4 to 10 are used. According to the literature, the most preferred 8 grade ranges.

Some researchers preferred to use 4 main aspects in the aspect parameter they used in their studies. There are researchers who use the aspects divided into 4 groups in their studies in different ways. According to Temesgen et al. [110] used 4 cardinal directions: north, south, east and west. Özşahin and Kaymaz [111] have 4 classes; they used it by arranging it as straight/N-NE-NW/S-SE-SW/E-W. There are studies that use the aspect by classifying it in 5 ways [6, 97, 105, 112].

In the literature, three different directions were found in the 5-category. The first of these; flat (−1°), north (315°-360°, 0°-45°), east (45°-135°), south (135°-225°) and west (225°-315°) [113]. The second classification is; (1) SW 1810 –2250 , (2) SE 1360 –1800 , (3) ESE 910 –1350 and SWW 2260 –2700 , (4) NEE 460 –900 and WNW


**Table 1.**

*Slope directions and angles.*

2710 –3150 , (5) NNE 00 –450 and NWN 3160 –3600 [74]. The third and final classification is; It is flat, NE, SE, SW and NW [50].

Aspect maps divided into 6 classes are very common in the literature. Kumtepe et al. [114] prepared this classification as 0–60°, 60–120°, 120–180°, 180–240°, 240–300°, 300–360°.

The second most preferred classification in the literature is 8 classes prepared with groups of 450 divided into equal class intervals [35, 43, 45, 54, 65, 79, 94, 95]. This classification; N (337.5–22.5), NE (22.5–67.5), E (67.5–112.5), SE (112.5–157.5), S (157.5–202.5), SW (202.5–247.5), W (247.5–292.5) and NW (292.5–337.5) [37]. Ramakrishnan, et al. [56], on the other hand, arranged the 8-class classification differently as 45–90, 90–135, 135–180, 180–225, 225–270, 270–155 and 315–360 degrees.

**Figure 1.** *Distribution of class range values used according to the literature.*

**Figure 2.** *Distribution of landslide areas according to directions.*

#### *The Effect of Aspect on Landslide and Its Relationship with Other Parameters DOI: http://dx.doi.org/10.5772/intechopen.99389*

According to the literature, the most preferred classification is groups of 9 [11, 32, 47, 52, 53, 66, 68, 69, 71, 87, 93, 95, 109, 102, 113]. In studies, this classification is; flat area (−1°), north (337.5° -22.5°), northeast (22.5° -67.5°), east (67.5° -12.5°), southeast (112.5° -12.5°) 157.5°), south (157.5° -202.5°), southwest (202.5° -247.5°), west (247.5° -292.5°), and northwest (292.5° -337.5°) [49, 46, 67, 73]. According to Rozos et al. [74] is this group; They used NNE, NEE, SEE, SSE, SSW, SWW, NWW, NNW, as flat shapes. The interesting thing about this classification is that the surface is displayed from 2 different angles.

The graph in **Figure 1** was prepared using the literature data. It is seen that the most used classification is the groups of 56% and 9 percent. Again, it is seen from the graph that the group of 1 to 4 is the least used class.

According to the literature, the most used direction classes are given in **Figure 2**. The direction of the landslide areas varies according to the study areas. However, in the studies examined, it is understood that the directions where landslides occur most are the slopes facing south and west. The probability of landslides in other directions is almost equal. In some studies, landslides were encountered at an equal level in all directions.

#### **5. Conclusions**

In this study, the use of aspect parameter in landslide susceptibility studies and its effect on landslide were investigated. It is one of the parameters that cannot be agreed upon by the researchers. While some researchers associate landslide occurrences in the study area with this parameter, some researchers argued that landslides are equally distributed in all directions and that the parameter is ineffective.

It is a fact that this parameter should not be evaluated alone, as in other parameters. The parameter is the predisposing factor for the triggers. One of these triggers is precipitation. There are many studies showings that intense landslides occur on slopes that receive rainfall. Climatic events such as sun, wind, snow water, freeze– thaw are also associated with the aspect parameter. The other two parameters most associated with climatic factors are geology and vegetation.

The other subject discussed in the study is the relationship of the directions with each other and with the landslide. The most common landslides seen in the studies examined are south and north directions. There is an opposite relationship between them. If there are frequent landslides on the south-facing slopes, there are almost no landslides on the north-facing slopes. Again, on the contrary, if landslides are concentrated on the north-facing slopes, landslides are not expected in the southern part. If a landslide occurs more in the south, it is associated with sun exposure, drought and lack of vegetation. Those occurring in the north are mostly evaluated by heavy rainfall, humidity and the water holding capacity of the soil.

Finally, the class ranges used in the literature are included in the study. Aspects used in the literature. In the studies, this classification is; flat area (−1°), north (337.5° -22.5°), northeast (22.5° -67.5°), east (67.5° -12.5°), southeast (112.5° -12.5°) 157.5°), south (157.5°) ° -202.5°), southwest (202.5° -247.5°), west (247.5° -292.5°) and northwest (292.5° -337.5°). Depending on the user's preference, some prefer the main classes, while others include intermediate aspects in their work. Some studies do not include aspects that do not appear to have landslides in their studies. In this way, various classifications such as 4, 5, 6, 8 and 9 are used. While the most preferred 9 classes are the least preferred groups of 4. With this study, the use of the aspect parameter in landslide susceptibility studies and its effect on the landslide together with other parameters were revealed.

*Landslides*

#### **Author details**

Seda Cellek Kirsehir Ahi Evran University, Kirsehir, Turkey

\*Address all correspondence to: sedacellek@ahievran.edu.tr

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*The Effect of Aspect on Landslide and Its Relationship with Other Parameters DOI: http://dx.doi.org/10.5772/intechopen.99389*

#### **References**

[1] Yomralioglu, T., Handbook of Geographic Information Systems: Basic Concepts and Applications, 2009. 480 p, ISBN 975-97369-0-X, Istanbul.

[2] Tanoli JI, Ningsheng C, Regmi AD, Jun L. Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi, Nepal. Arabian Journal of Geosciences. 2017; 10-13. DOI:10.1007/ s12517-017-3026-9.

[3] Chen SC, Chang CC, Chan HC, Huang LM, Lin LL. Modeling typhoon event-induced landslides using GISbased logistic regression: A case study of Alishan Forestry Railway, Taiwan. Math. Prob., Eng. 2013. Available from: https://www.hindawi.com/journals/ mpe/2013/728304/.

[4] Saha AK, Gupta RP, Sarkar I, Arora M K, Csaplovics E. An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides. 2005; 2:61-69.

[5] Lee S. Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using gis environmental management. Springer Science-Business. 2004;34,2: 223-232.

[6] Bourenane H, Bouhadad Y, Guettouche MS, Braham M. GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bulletin of Engineering Geology and the Environment. 2015; 74:337-355.

[7] Zhuang J, Peng C, Wang G, Chen X, Iqbal J, Guo X. Rainfall thresholds for the occurrence of debris flows in the Jiangjia Gully, Yunnan Province, China. Eng. Geol*.* 2015;195. doi.org/10.1016/j. enggeo.2015.06.006.

[8] Lee S. Application of logistic regression model and its validation for landslide susceptibility mapping using gis and remote sensing data. International Journal of Remote Sensing. 2005; 26, 7-10: 1477-1491.

[9] Hadji R, Chouabi A, Gadri L, Rais K, Hamed Y, Boumazbeur A. Application of linear indexing model and GIS techniques for the slope movement susceptibility modeling in Bousselam upstream basin Northeast Algeria. Arabian J. Geosci. 2016; 9:3,192. doi. org/10.1007/s12517-015-2169-9.

[10] Carrara A. Multivariate methods for landslide hazard evaluation. Math. Geol. 1983; 15:3, 403- 426.

[11] Chen W, Pourghasemi HR, Kornejady A, Zhang N. Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma. 2017; **305**: 314-327. doi.org/ doi.org/10.1016/j.geoderma.2017. 06.020.

[12] Chen W, Pourghasemi HR, Kornejady A, Xie X. GIS-based landslide susceptibility evaluation using certainty factor and index of entropy ensembled with alternating decision tree models. In book: Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Adv. Nat. Technol. Hazards Res. 2018; **48**.

[13] Nagarajan R, Roy A, Vinod Kumar R, Mukherjee A, Khire MV. Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull. Eng. Geol. Environ. 2000; **58**:275- 287. doi.org/10.1007/s100649900032.

[14] Fernández T, Irigaray C, El Hamdouni R, Chacón J. Methodology for landslide susceptibility mapping by means of a GIS application to the

Contraviesa Area (Granada, Spain). Natural Hazards. 2003; 30: 297-308.

[15] Santacana N, Baeza B, Corominas J, Paz A, Marturia J. A GIS–Based Multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla De Lillet Area (Eastern Pyrenees, Spain). Natural Hazards. 2003; 30: 281-295.

[16] Ayalew L, Yamagishi H, Ugawa N. Landslide susceptibility mapping Using GIS based weighted linear combination, the case in Tsugawa Area of Agano River, Niigata Prefecture, Japan. Springer-Verlag, Landslides. 2004; 1:73-81.

[17] Neuhäuser B, Terhorst B. Landslide susceptibility assessment using "weights of-evidence applied to a study area at the Jurassic Escarpment (SW-Germany). Geomorphology. 2007; 86: 12-24.

[18] Blesius L, Weirich F. Shallow landslide susceptibility mapping using stereo air photos and thematic maps. Cartography and Geographic Information Science. 2010; 37: 2.

[19] Greenbaum D, Ton Tu M, Bowker MR, Browne TJ, Buleka J, Greally KB, Kuna G, Mcdonald AJW, Marsh SH, Northmore KJ, O'connor EA, Tragheim DG. Rapid methods of landslide hazard mapping: Papua New Guınea case study. British Geological Survey: Technical Report: Wc/95/27 Overseas Geology Series, I. Eyworth, Nottingham, British Geological Survey, 1995, Available from: Https://Core. Ac.Uk/Download/Pdf/57306.Pdf

[20] Go´Meza H, Kavazoğlu T. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology; 2004:78,11-27.

[21] Jimenez-Peralvarez JD, Irigaray C, El Hamdouni R, Chacon J. Building

models for automatic landslidesusceptibility analysis, mapping and validation in ArcGIS. Nat Hazards. 2009; 571 – 590.

[22] Anbalagan R. Landslide hazard evaluation and zonation mapping in Mountainous Terrain. Engineering Geology. 1992; 32: 269-277.

[23] Van Westen CJ, Bonilla JBA. Mountain hazard analysis using a PC-Based GIS. Proceedings of the 6th International Congress of Engineering Geology. 1990; 265-271.

[24] Carrara A. Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P. GIS Techniques and Statistical Models in Evaluating Landslide Hazard, Earth Surface Processes and Landforms. 1991;16: 5, 427-445.

[25] Koukis G, Ziourkas C. Slope instability phenomena in Greece: A Statistical Analysis. Bulletin of International Association of Engineering Geologists. 1991; 47-60.

[26] Juang CH, Lee DH, Sheu C. Mapping Slope Failure Potential Using Fuzzy Sets. J. Geotech. Eng. Div. ASCE. ;1992: 118, 475-493.

[27] Pachauri AK, Pant M. Landslide hazard mapping based on geological attributes. Eng. Geol. 1992;32: 81-100. doi.org/10.1016/0013-7952(92)90020-Y.

[28] Maharaj R. Landslide processes and landslide susceptibility analysis from an Upland Watershed: a case study from St. Andrew, Jamaica, West Indies. Engineering Geology. 1993; 34: 53-79.

[29] Mejia-Navarro M, Wohl EE. Geological hazard and risk evaluation using GIS: Methodology and model applied to Medellin, Columbia. Bulletin of Association of Engineering Geologists. 1994; 31, 4: 459-481.

[30] Guzzetti F, Carrara A, Cardinali M, Reichenbach P. Landslide hazard

*The Effect of Aspect on Landslide and Its Relationship with Other Parameters DOI: http://dx.doi.org/10.5772/intechopen.99389*

evaluation: a review of current techniques and their application in a multi–scale study, Central Italy. Geomorphology. 1999; 31: 181-216.

[31] Luzi L, Pergalani F. Slope instability in static and dynamic conditions for urban planning: the "Oltre Po Pavese" case history (Regione Lombardia-Italy). Natural Hazards. 1999; 20:57-82.

[32] Kavzoglu T, Sahin EK, Colkesen I. Landslide susceptibility mapping using GISbased multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. 2014; 11:425-439.

[33] Fan JR, Zhang XY, Su FH, Ge YG, Tarolli P, Yang ZY, Zeng C, Zeng Z. Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data. J. Mt. Sci*.* 2017; 14(9):1677-1688. doi. org/10.1007/s10346-017-0927-3.

[34] Liu C, Li W, Wu H. Susceptibility evaluation and mapping of China's landslides based on multisource data. Natural Hazards. 2013;1477-1495.

[35] Ahmed B. Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides. 2014;12 (6): 1077-1095.

[36] Fernandez Merodo JA, Pastor M, Mira P, Tonni L, Herreros MI, Gonzalez E, Tamagnini R. Modelling of diffuse failure mechanisms of catastrophic landslides. Computer Methods in Applied Mechanics and Engineering. 2004; 193: 2911-2939.

[37] Balamurugan G, Ramesh V, Touthang M. Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India. Nat Hazards. 2016.doi.org/10.1186/s40677- 014-0009-y 84, 465-488.

[38] Pourghasemi H R, Rossi M. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol. 2017;130: 609-633.

[39] Dai FC, Lee CF, Li J, Xu ZW. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology. 2001; 40:3: 381– 391.

[40] Sarkar S, Kanungo DP. GIS application in landslide susceptibility mapping of Indian Himalayas, GIS. Landslide. 2017; 211-219.

[41] Constantin M, Bednarik M, Jurchescu MC, Vlaicu M. Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ. Earth Sci. 2011; 63 (2) 397-406. doi.org/10.1007/ s12665-010-0724.

[42] Pourghasemi HR, Mohammady M, Pradhan B. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena. 2012; 97: 71-84.

[43] Pawluszek K, Borkowski A. Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland. Nat. Hazards. 2017; 86 (2) 919-952. doi.org/10.1007/ s11069-016-2725-y.

[44] Ruff M, Czurda K. Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria). Geomorphology. 2008; 94: 3: 314-324.

[45] Youssef AM, Al-Kathery M, Pradhan B. Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based

frequency ratio and index of entropy models. Geosci. J. 2015;19(1)113-134.

[46] Hasekioğulları GD. Assessment of parameter effects in producing landslide susceptibility maps. Master Thesis (in Turkish) Hacettepe University, Turkey, 2011.

[47] Devkota KC, Regmi AD, Pourghase H, Yoshida K, Pradhan B, Ryu IC. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat. Hazards. 2013; 65:1:135-165.

[48] Lee S, Min K. Statistical analyses of landslide susceptibility at Yongin, Korea. Environmental Geology. 2001;40 (9)1095-1113. doi.org/10.1007/ s002540100310.

[49] Dai FC, Lee CF. Landslides on natural terrain physical characteristics and susceptibility mapping in Hong Kong. Mountain Research and Development. 2002; 22: 1: 40-47.

[50] Timilsina M, Bhandary NP, Dahal RK, Yatabe R. Distribution probability of large-scale landslides in central Nepal. 2014; 226: 1: 236-248. Available from: https://doi. org/10.1016/j.geomorph.2014.05.031.

[51] Saha AK, Gupta RP, Arora MK. GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens. 2002; 23: 357-369.

[52] Kayastha P, Bijukchhen SM, Dhital MR, De Smedt F. GIS based landslide susceptibility mapping using a fuzzy logic approach: A case study from Ghurmi-Dhad Khola area, Eastern Nepal. Journal of the Geological Society of India. 2013;82: 249-261.

[53] Chen CW, Sait H, Oguchi T. Rainfall intensity–duration conditions for mass

movements in Taiwan, Prog. Earth Planet. Sci. 2015;2: 1-13. doi. org/10.1186/s40645-015-0049-2.

[54] Saponaro A, Pilz, Wieland, Bindi D, Moldobeko B, Parola B. Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan. Bulletin of Engineering Geology and the Environment. 2015;74: 1117-1136.

[55] Myronidis D, Papageorgiou C, Theophanous S. Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat. Hazards. 2016;245-263.

[56] Ramakrishnan D, Singh TN, Verma AK, Gulati A, Tiwari KC. Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Natural Hazards. 2013; 65:315-330.

[57] Lee S, Choi J, Mi K. Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. International Journal of Remote Sensing. 2004; 25 (11) 2037-2052. doi. org/10.1080/01431160310001618734.

[58] Chuan T, Jing Z, Jingtao L. Emergency assessment of seismic landslide susceptibility: a case study of the 2008 Wenchuan earthquake affected area. Earthquake. Engineering and Engineering Vibration. 2009; 8:28: 207-217.

[59] Bednarik M, Magulova B, Matys M, Marschalko M. Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulas railway case study. Phys Chem Earth Parts. 2010; A/B/C 35(3-5): 162– 171.

[60] Kumar R, Anbalagan R. Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region Uttarakhand. Journal of the Geological Society of India. 2016;87 (3) 271-286

*The Effect of Aspect on Landslide and Its Relationship with Other Parameters DOI: http://dx.doi.org/10.5772/intechopen.99389*

[61] Magliulo P, Di Lisio A, Russo F. Comparison of GIS-Based Methodologies for the Landslide Susceptibility Assessment, Geoinformatica. 2009; 13:253-265.

[62] Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro fuzzy inference system and GIS. J. Comp. Geosci. 2011;45: 199-211. doi. org/10.1016/j.cageo.2011.10.031.

[63] Yang ZH, Lan HX, Gao X, Li, LP, Men YS, Wu YM. Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat. Hazard. 2015; 75(3)2467-2487. doi.org/10.1007/ s11069-014-1441-8.

[64] Ruff M, Czurda K. Landslide Susceptibility Analysis with a Heuristic Approach at the Eastern Alps (Vorarlberg, Austria). Geomorphology. 2008; 94: 3-4: 314-324.

[65] Meinhardt M, Fink M, Tünschel H. Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology. 2015;234: 80-97.

[66] Zhang JQ, Liu RK, Deng W, Khanal NR, Gurung DR, Ramachandra Sri, Murthy M, Wahid S. Characteristics of landslide in Koshi River Basin, Central Himalaya. Journal of Mountain Science. 2016; 1711-1722.

[67] Wang HQ, He J, Li Y, Sun S. Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environ. Earth Sci*.* 2016; 75: 422. doi. org/10.19111/bulletinofmre.502343.

[68] Alexakis DD, Agapiou A, Tzouvaras M, Themistocleous K, Neocleous K, Michaelides S, Hadjimitsis DG. Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus. Natural Hazards. 2014;72: 1: 119-141,

[69] Kouli M, Loupasakis C, Soupios P, Rozos D, Vallianatos F. Landslide susceptibility mapping by comparing the WLC and WofE mutli-criteria methods in the West Crete Island, Greece. Environ Earth Sci. 2014.

[70] Ayalew L, Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Central Japan. Geomorphology. 2005; 65: 1-2: 15-31.

[71] Pham BT, Bui DT, Prakash I, Dholakia MB. Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 2017;149: 52-63. htdoi. org/10.1016/j.catena.2016.09.007.

[72] Kritikos T, Davies T. Assessment of rainfall-generated shallow landslide/ debris-flow susceptibility and runout using a GIS-based approach: Application to western Southern Alps of New Zealand. Landslides. 2014;12 (6)1051-1075. doi.org/10.1007/ s10346-014-0533-6.

[73] Ilia I, Tsangaratos P. Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides. 2016; 379-397.

[74] Rozos D, Bathrellos GD, Skilodimou HD. Landslide susceptibility mapping of the northeastern part of Achaia Prefecture using Analytical Hierarchical Process and GIS techniques. Bull. Geol. Soc. Greece, 2010.Proceedings of the 12th

International Congress, Patras may, XLIII, 3, 1637-1646.

[75] Wilson JP, Gallant JC. Digital terrain analysis, Chapter 1, In., Eds. Terrain analysis: Principles and applications. New York. 2000; 1-27.

[76] Avcı V. Landslide susceptibility analysis of Esence Stream Basin (Bingöl) by weight- of- evidence method. International Journal of Social Science. 2016; 287-310.

[77] Rozos D, Pyrgiotis L, Skias S, Tsagaratos P. An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory: an application in Karditsa County. Landslides. 2008; 5(3):261-270.

[78] Calligaris C, Poretti G, Tariq S, Melis, MT. First steps towards a landslide inventory map of the Central Karakoram National Park. European Journal of Remote Sensing. 2017; 46:1, 272-287. https://www.tandfonline.com/ doi/pdf/10.5721/EuJRS20134615.

[79] Rahman G, Atta-ur-Rahman S, Collins A E. Geospatial Analysis of landslide susceptibility and zonation in Shahpur Valley, Eastern Hindu Kush using Frequency Ratio Model. Proceedings of the Pakistan Academy of Sciences: Pakistan Academy of Sciences B. Life and Environmental Sciences. 2017;54 (3): 149-163. Available from: https://www.paspk.org/ wp-content/uploads/2017/09/ Geospatial-Analysis-of-Landslide.pdf

[80] Sidle R, Ochiai H. Landslides: Processes, Prediction, and Land Use**.**, Geography. 2006Book chapter, https://www.researchgate.net/ publication/292653165\_Landslides\_ Processes\_Prediction\_and\_Land\_Use

[81] Kornejady A, Ownegh M, Bahreman. Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena. 2017; 152:144-162, doi: 10.1016/j.catena.2017.01.010.

[82] Bijukchhen SM, Kayastha P, Dhital, MR. A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi-Dhad Khola, East Nepal. Arabian J. Geosci., 2013; 6: 2727-2743. doi.org/10.1007/s12517- 012-0569-7.

[83] Görüm T. Landslide susceptibility analysis with geographic information systems and statistical methods: Melen Gorge and near vicinty. İstanbul University, Master Thesis, Istanbul (unpublished), 2006.

[84] Remondo J, Gonzalez-Diez A, Teran JRD, Cendrero A. Landslide susceptibility models utilising spatial data analysis techniques: a case study from the lower Deba Valley, Guipúzcoa (Spain). Natural Hazards. 2003;30: 267-279.

[85] Tasoglu İK, Keskin Çıtıroglu H, Mekik Ç. GIS-based landslide susceptibility assessment: A case study in Kelemen Valley (Yenice-Karabuk, NW Turkey). Environ. Earth Sci. 2016; 75: 1295. https://doi.org/10.1007/ s12665-016-6098-z.

[86] Afungang RN, Nkwemoh C, Ngoufo R. Spatial modelling of landslide susceptibility using logistic regression model in the Bamenda Escarpment Zone, NW Cameroon. Internatıonal Journal of Innovatıve Research & Development. 2017; 6 :2:187-199.

[87] Yeşiloğlu N. Eğirdir (Isparta) yerleşim merkezi için heyelan olası tehlike değerlendirmesi ve haritalaması, yüksek lisans tezi. Hacettepe Üniversitesi, Ankara, 270p, 2006.

[88] Tang C, Zhu J, Qi X, Ding J. Landslides induced by the Wenchuan earthquake and the subsequent strong

#### *The Effect of Aspect on Landslide and Its Relationship with Other Parameters DOI: http://dx.doi.org/10.5772/intechopen.99389*

rainfall event: A case study in the Beichuan area of China. Eng. Geol. 2011; 122: 22-33. doi.org/10.1016/j. enggeo.2011.03.013.

[89] Yang ZH, Lan HX, Gao X, Li LP, Meng YS, Wu, YM. Urgent landslide susceptibility assessment in the 2013 Lushan earthquakeimpacted area, Sichuan Province, China. Nat Hazards. 2015; 2467-2487.

[90] Guillard C, Zezere J. Landslide Susceptibility assessment and validation in the framework of municipal planning in Portugal: The Case of Loures Municipality, Environmental management. 2012; 50: 721-735. Available from: https://link.springer. com/article/10.1007/s00267-012-9921-7.

[91] Sidle RC. Influence of Forest Harvesting Activities on Debris Avalanches and Flows. 1985. Available from: http://citeseerx.ist.psu.edu/ viewdoc/download?doi=10.1.1.512.8208 &rep=rep1&type=pdf.

[92] Ramesh V, Anbazhagan S. Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models. Environ Earth Sci. 2015; 8009-8021.

[93] Dahal RK. Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya. Environmental Earth Sciences. 2014;71: 12:5145, 5164.

[94] Champati ray PK, Dimri S, Lakhera RC, Kumar Sati S. Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides. 2007;4(2):101-111,

[95] Srivastava V, Srivastava HB, Lakhera RC. Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India. 2010; 1:3. https://www.tandfonline.com/doi/

citedby/10.1080/19475705.2010.490103? scroll=top&needAccess=true

[96] Yılmaz G. Afete duyarlı planlama kapsamında planlama jeorisk ilişkisi ve CBS ile analizi, Bartın Kenti Örneği. Yüksek Lisans Tezi, Gazi Üniversitesi, Ankara, 2008.

[97] Özşahin E. Landslide susceptibility analysis by geographical information systems: the case of Ganos Mount (Tekirdağ) (in Turkish). Electron. J. Map Technol*.* 2015; 7 (1) 47-63. doi. org/10.15659/hartek.15.04.68.

[98] Rajakumar P, Sanjeevi S, Jayaseelan S, Isakkipandian G, Edwin M, Balaji P, Ehanthalingam G. Landslide susceptibility mapping in a hilly terrain using remote sensing and GIS. Journal of the Indian Society of Remote Sensing. 2007; 35: 31-42. Available from: https://link.springer. com/article/10.1007/BF02991831.

[99] Hong H, Naghibi S A, Pourghasemi HR, Pradhan B. GIS-based landslide spatial modeling in Ganzhou city, China. Arab J Geosci. 2016; 9: 2.1: 26.

[100] Tombuş FE. Uzaktan algılama ve cografi bilgi sistemleri kullanılarak erozyon risk belirlemesine yeni bir yaklaşım, Çorum ili örneği. Yüksek Lisans Tezi, Anadolu Üniversitesi, Eskişehir. 2005.

[101] Ercanoglu M, Temiz AF. Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environmental Earth Sciences. 2011: doi:10.1007/ s12665-011-0912-4

[102] Yalçın A. Ardeşen (Rize) yöresinin heyelan duyarlılığı açısından incelenmesi. Doktora Tezi, Karadeniz Teknik University, Trabzon. 2005.

[103] Lineback GM, Marcus WA, Aspinall R, Custer SG. Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology. 2001; 37: 149-165.

[104] Wang S.Q., Unwin D.J., 1992. Modelling landslide distribution on loess soils in China: an investigation. International Journal of Geographical Information Systems 6:391-405.

[105] Tangestani MH. Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Australian J. Earth Sci. 2004; 51: 439-450. doi.org/10.1111/j.1400- 0952.2004.01068.x.

[106] Marston R, Miller M, Devkota L. Geoecology and mass movements in the Manaslu Ganesh and Langtang-Jural Himals, Nepal. Geomorphology. 1998; 26: 139– 150.

[107] Akıncı H, Kılıçoğlu C. Production of landslide susceptibility map of Atakum (Samsun) district. MÜHJEO'2015: National Engineering Geology Symposium, 3-5 September 2015, Trabzon.

[108] Yeşilnacar E, Topal T. Landslide. Susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey). Engineering Geology. 2005; 79: 251-266.

[109] Çevik E, Topal T. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology. 2003; 949-962.

[110] Temesgen B, Mohammed MU, Korme T. Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet Area, Ethiophia. Phys. Chem. Earth. 2001; 26-9: 665-675.

[111] Özşahin E, Kaymaz ÇK. Landslide susceptibility analysis of Camili (Macahel) Biosphere Reserve Area (Artvin, NE Turkey). Turkish Studies - International Periodical For The Languages, Literature and History of Turkish or Turkic. 2013; 8(3)471-493. doi.org/10.7827/TurkishStudies.4260.

[112] Caniani D, Pascale S, Sdao F, Sole A. Neural networks and landslide susceptibility: a Case study of the urban area of Potenza. Natural Hazards. 2008; 45: 55– 72.

[113] Avcı V. Analysis of landslide succeptibility of Manav Stream Basin (Bingöl). The Journal of International Social Research. 2016; 9:42-9. doi: 10.17719/jisr.20164216199.

[114] Kumtepe P, Nurlu Y, Cengiz T, Sütçü E. Bolu çevresinin heyelan duyarlılık analizi [Bildiri]. TMMOB Coğrafi Bilgi Sistemleri Kongresi, 02-06 Kasım 2009, İzmir.

#### **Chapter 3**
