Landslide Susceptibility Assessment in Constantine Region (NE Algeria) By Means of Statistical Models

Open access

Abstract

The purpose of the present study was to compare the prediction performances of three statistical methods, namely, information value (IV), weight of evidence (WoE) and frequency ratio (FR), for landslide susceptibility mapping (LSM) at the east of Constantine region. A detailed landslide inventory of the study area with a total of 81 landslide locations was compiled from aerial photographs, satellite images and field surveys. This landslide inventory was randomly split into a testing dataset (70%) for training the models, and the remaining (30%) was used for validation purpose. Nine landslide-related factors such as slope gradient, slope aspect, elevation, distance to streams, lithology, distance to lineaments, precipitation, Normalized Difference Vegetation Index (NDVI) and stream density were used in the landslide susceptibility analyses. The inventory was adopted to analyse the spatial relationship between these landslide factors and landslide occurrences. Based on IV, WoE and FR approaches, three landslide susceptibility zonation maps were categorized, namely, “very high, high, moderate, low, and very low”. The results were compared and validated by computing area under Road the receiver operating characteristic (ROC) curve (AUC). From the statistics, it is noted that prediction scores of the FR, IV and WoE models are relatively similar with 73.32%, 73.95% and 79.07%, respectively. However, the map, obtained using the WoE technique, was experienced to be more suitable for the study area. Based on the results, the produced LSM can serve as a reference for planning and decision-making regarding the general use of the land.

1 Introduction

Natural hazards comport all injurious geophysical events, such as earthquakes, flooding and landsliding [1, 2, 3, 4, 5, 6, 7, 8].

Landslides occur worldwide; however, their frequency/ intensity is greater in countries with mountainous and hilly environments [9, 10]. Only for earthquakes and movements of land, the NE of Algeria is yearly affected by loss of property greater than that caused by any other problem, especially in the Constantine province, where recurrent slope failures caused severe damages [11, 12]. To the east of the chief town, the A1 highway and its neighbouring have been affected by spectacular mass movements during and after its construction [13].

Landslide susceptibility assessment has become a major research topic in the last few decades [14]. It is often performed through the identification, analysis and combination of landslide causative factors. It is generally approached by two broad techniques: (i) qualitative approaches, based on expert knowledge, and (ii) quantitative approaches based on statistical analysis [15, 16, 17, 18, 19]. Nowadays, the quantitative approaches are the most used. They are based on mathematical expressions of the relationship between conditioning factors and the landslides, usually managed as thematic data within geographic information system (GIS). Their two main branches include deterministic methods, more appropriate for large scale [20], and statistical methods, valid for small and medium scale, such as weight of evidence (WoE), information value (IV), frequency ratio (FR), fuzzy logic (FL), logistic regression (LR) and artificial neural network (ANN) approaches [21, 22]. So far, there is no agreement that which one is the best, but the general consensus is that each method has its advantages and disadvantages [23, 24].

The aims of this study were to assess landslide susceptibility for the east of Constantine province, using the WoE, IV and FR models, and to measure their performances based on the receiver operating characteristic (ROC) analysis [25]. This information could be used by land planners to estimate the threats to population, property and transportation network.

The study area is located the east of the Constantine province (NE Algeria), known as one of the most landslide prone areas in NE of Algeria. It frames 6°37‘30“ to 6°49‘30“longitude and, 36°16‘30“to 36°27‘35“ latitude and spans on 351 km2. The altitude of the landscape ranges from 500 m to 1200 m a.s.l. distinguishing several mountains such as Jebel Kellal (950 m), Jebel El Ouahche (1100 m) and Kef El Akahl (1200 m), culminating on Boumerzoug, Hamimin, El Aria and Ennaga wadies.

The study area is characterized by a complex morphology and a semi-arid climate with two typical rainy and dry seasons in contrast [26]. The precipitation ranges between 450 and 500 mm/year [27]. About 63% of the annual rainfall quantity is concentrated between December and February [28]. Geologically, the studied area is characterized by superposition of thrust sheet units made up from the base to the top by neritic unit (cretaceous carbonate); ultra-Tellian unit (cretaceous-eocene marls and marly limestone); Tellian sensu-stricto (s.s): marly dominance (Cretaceous-Eocene); Numidian unit with sandstone Burdigalian, clay and flysch (Eocene) and Mio-Plio-Quaternary: sandy clays, marls and conglomerate (Mio-Pliocene) [47]. Alluvial terraces and lacustrine calcareous formations with the Quaternary age. This structure was deposited during the Eocene and Miocene paroxysmal compressional phases (Fig. 1).

Figure 1
Figure 1

Geological map of the study area.

Citation: Studia Geotechnica et Mechanica 40, 3; 10.2478/sgem-2018-0024

2 Materials and Methodology

2.1 Landslide characteristics and inventory map

Preparing landslide maps is a preliminary step in landslide susceptibility assessment [29]. This allows to control the distribution, extent, types and patterns of landslides in relation to geomorphological, geological and environmental parameters [30]. The inventory map of the study area enumerates 81 slope failures (78 rotational slides, a planar slide, a solifluction and a debris flow) (Fig. 2a). It was carried out depending on the surveys conducted by the Algerian of Highways (ADA), as well as the visual interpretation of air photos and satellite images. During field studies, all landslides were checked and mapped with the aid of a global positioning system (GPS). The slope failures seem to be more frequent in the NE part of the study area, dominated by weak formations. In this research, the landslide inventory was randomly divided into 70% (57 events) for training the model and 30% (24 events) for its validation (Fig. 3) [21].

Figure 2
Figure 2

Optical remote sensing images and air photo used for the landslides determination in the study area: a- Panchromatic satellite image. b- A landslide occurred in highway’s segment before construction (air photo). c- A neighboring road affected by a rotational landslide.

Citation: Studia Geotechnica et Mechanica 40, 3; 10.2478/sgem-2018-0024

Figure 3
Figure 3

Landslide inventory map of the study area.

Citation: Studia Geotechnica et Mechanica 40, 3; 10.2478/sgem-2018-0024

2.2 Conditioning factors related to landslides

The geoenvironmental characteristics of an area control the initiation of slope failures in many ways. They could be considered as conditioning factors in the phenomenon prediction. In our research, both predisposition and triggering factors were selected among those most commonly used in landslide susceptibility assessment [31]. The data source used in this study comes from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, aerial photographs, geologic and topographic maps, etc. A contour map at 10 m intervals was digitized from (1/25000) topographic map and subsequently employed for generating a digital elevation model (DEM) of the study area. The related physiographic features were directly extracted from the DEM to construct the landslide susceptibility models.

2.2.1 Lithology

Geologic factors play an important role in slope failures and consequently in landslide susceptibility assessment [32]. The lithological map of the study area (Fig. 4a) was defined by the digitization of outcrop formation from “El Aria” geological map (No 74) published by the Algerian Geological Survey Agency (ASGA). The variability of the lithologies in the study area is classified into seven main lithological groups.

Figure 4
Figure 4

Landslide conditioning factor used for landslide susceptibility mapping in the study area: a- Lithology. b- Slope angle. c- Slope aspect. d- Elevation.

Citation: Studia Geotechnica et Mechanica 40, 3; 10.2478/sgem-2018-0024

2.2.2 Slope gradient

The most important intrinsic factor influencing slope stability is the steepness of the slope [33]. The slopes in

the study area were divided into seven categories based on an interval of 5° (Fig. 4b).

2.2.3 Slope aspect

The slope aspect usually plays a significant role in controlling some related climatic factors such as rain exposure, soil moisture and weathering [34]. The aspect map was classified into eight main classes, with the addition of flat lands (Fig. 4c).

2.2.4 Elevation

In many referential studies in landslide susceptibility, elevation was considered as an influential factor in slope stability [35]. The elevation in the study area was divided into seven categories based on an interval of 100 m (Fig. 4d).

2.2.5 Distance to lineaments

Lineaments play an important role in landslide initiation [36]. Based on digitized faults, folds, and fractures, the distance to lineaments map was classified into eight lengths from 400 to 3200 m, at 400 m steps, with an additional ninth category for distances greater than 3400 m (Fig. 4e).

2.2.6 Distance to streams and stream density

The streams network is an important controlling factor of landslide occurrence [37], as its erosion action may undercut the foot of slopes [38]. The distance to the stream was classified into four lengths from 200 to 800 m, at 200 m steps, with an additional fifth category >1000 m (Fig. 4f). The map interpreting the Kernel density of streams was processed from the terrain hydrographic network features (Fig. 4g).

2.2.7 Rainfall

The most important triggering factor influencing the slope stability is the rainfall [39]. The annual average rainfall map was obtained from the inverse distance weight (IDW) interpolation of data from five hydroclimatic stations (Constantine, Hamma Bouziane, Aïn El Bey, Fourchi and Bir Drimil) over a 32-year period. The study areas get precipitations from 500 to 650 mm of rainfall (Fig. 4h).

2.2.8 Normalized Difference Vegetation Index (NDVI)

The NDVI is considered an influencing factor in landslide susceptibility assessment as it estimates the vegetation density [40]. The NDVI map (Fig. 4i) was calculated from Landsat 7 ETM+ scene by using a non-linear transformation of the red and near-infrared bands of satellite images, using the following formula:

NDVI=(NIRR)/(NIR+R)

where NIR and R are the reflectance in the near-infrared and red portions of the electromagnetic spectrum, respectively.

2.3 Methodology

In this research, landslide susceptibility mappings (LSMs) were designed by means of landslide inventory and FR, WoE and IV techniques. In order to evaluate the consistency of the three models, area under the ROC curve (AUC) was plotted using the Microsoft Excel software [41]. This method is commonly used to measure how much the model was successful in modelling and predicting the results. All LSMs were classified into five landslide susceptibility classes using Natural Breaks (NB) classifier [14].

2.3.1 Weight of evidence

The WoE is a statistical technique for landslide susceptibility assessment that uses landslide occurrence as training points to derive prediction outputs [42]. This method uses the log-linear from the Bayesian probability model in order to estimate the relative importance of evidence by a statistical mean, as given in the following equation [43].

P(A|B)=P(B|A)*P(A)P(B)

Based on the presence or absence of the landslide within the area (A), this method calculates the weight for each landslide predictive factor (B) [44], and it is written as

Wi+=ln(P{B|A}P{B|A¯})
Wi=ln(P{B¯|A}P{B¯|A¯})

where P is the probability, B is the presence of potential landslide predictive factor, B is the absence of potential landslide predictive factor and A is the presence and A is the absence of landslides. Positive (W+) and negative (W-) weights indicate the correlation between the presence of

the causative factor and landslides. The contrast weight is the difference between the W+ and W - (C=W+W-); the C factor reflects the total spatial correlation between landslides and the desired causative factor.

2.3.2 Information value

The IV is a very useful concept for variable selection during model building [45]. In this method, the weight for factor class is defined as the natural logarithm of the landslide density in the class divided by the density of landslide in total area. The formula for IV is shown in the following [46]:

Sif=ln(DifD)=ln[(NifPif/NP)]

where Sif is the weight given to a given class i of the factor f, Dif is the landslide density inside the class i of the factor f, D is the landslide density inside the total area, N if is the number of landslides in a given class i of the parameter f, Pif is the number of pixels in class i of the factor f, N is the total landslides within the study area and P is the total pixels inside the study area.

2.3.3 Frequency ratio

The FR is a useful method for variable integration while developing LSM [9]. It permits the derivation of spatial relationships between the landslide distribution and each causative factor. The FR is the ratio between landslides in factor class and the percentage of the area in the same class. The LSM is calculated by a summation of all factor ratios.

LSM=i=1nFRi

3 Result and Validation

3.1 Spatial relationship between causative factors and landslides

The resulted IV, WoE and FR susceptibility maps show close similarities between overall the three methods. The conditioning factors were classified into classes, and weights are presented in columns 7, 10 and 13 of Table 1.

Table 1

Spatial relationship between landslide and conditioning factors in the study.

FactorClassesLandslideIVFRWoE
pixels in classes(Sif)Dif(D)% of total area (a)% of landslide area (b)FRW+W-C
LithologyFlysch8395-0.343-28.6183.545.5232.310.71-0.3570.383-0.740
Clay74500.17514.6124.0728.671.190.1841.096-0.911
Marl51880.71059.299.8119.972.030.7622.150-1.388
Colluvium49500.0756.2217.6819.051.080.0781.525-1.447
Marly00.0000.001.090.000
limestone
Limestone00.0000.001.130.000
Travertine00.0000.000.700.000
Slope gradient (°)0–5°504-3.156-315.63100.045.521.940.04-3.2020.722-3.924
5–10°76030.19519.5424.0729.261.220.2061.089-0.883
10–15°98701.354135.379.8137.993.871.5031.993-0.490
15–20°4504-0.020-1.9917.6817.330.98-0.0211.541-1.562
20–25°22072.053205.311.098.4907.792.4514.829-2.377
25–30°6780.84284.151.132.612.320.9084.527-3.619
>30°6181.219121.930.702.383.381.3425.057-3.715
Rainfall (mm)50000.0000.0074.60.510.000---
550220190.22616.8667.6084.741.250.238-1.4761.715
6003964-0.606-45.2527.9815.260.55-0.6281.086-1.715
65000.0000.003.910.000
Distance to lineaments (m)0–400174090.0707.04100.062.4467.001.070.074-0.6340.708
400–80083770.49149.0619.7432.241.630.5221.264-0.743
800–120022-4.670-466.968.890.080.01-4.7162.372-7.089
1200–160088-2.616-261.554.630.340.07-2.6593.025-5.684
1600–200088-2.085-208.522.730.340.12-2.1273.558-5.684
2000–240000.0000.001.150.000
2400–280000.0000.000.340.000
2800–320000.0000.000.080.000
3200–360000.0000.000.000.000
Distance to streams (m)0–200230410.18313.5774.273.8688.681.200.193-1.8662.058
200–4002942-0.612-45.3820.8711.320.54-0.6341.425-2.058
400–60000.0000.004.120.000-
600–80000.0000.000.980.000
800–100000.0000.000.170.000
Elevation (m)500–6001347-0.424-25.0759.27.925.190.65-0.4402.466-2.906
600–7003040-0.520-30.7819.6811.700.59-0.5401.481-2.021
700–80082490.42224.9920.8131.751.530.4481.214-0.765
800–90086070.81147.9614.7333.122.250.8731.575-0.703
900–100044530.1609.4814.6017.141.170.1691.745-1.576
1000–1100288-2.576-152.4014.551.110.08-2.6201.872-4.492
1100–120000.0000.007.710.000
NDVI-0.974–0.23161-3.216-35.1110.95.880.240.04-3.2612.785-6.047
0.231–0.148154-3.861-42.1528.130.590.02-3.9071.216-5.123
0.148–0.045508-1.110-12.115.931.960.33-1.1412.774-3.915
0.045–0.15129320.0570.6210.6611.281.060.0592.122-2.062
0.151–0.818223280.5546.0549.4085.931.740.590-1.2201.810
Stream density0–0.2208870.61633.2253.91.8580.391.850.658-0.7521.411
0.2–0.43478-0.851-45.910.4313.390.43-0.8790.988-1.867
0.4–0.6492-2.057-110.920.131.890.13-2.0991.850-3.948
0.6–0.81126-0.426-22.990.654.330.65-0.4432.651-3.094
0.8–100.0000.000.000.000
1–1.200.0000.000.000.000
Slope aspectN73060.92265.7771.311.1828.122.510.9981.937-0.939
NE2162-0.157-11.239.748.320.85-0.1652.235-2.399
E2763-0.092-6.5511.6610.640.91-0.0962.032-2.129
SE1622-0.760-54.2113.356.240.47-0.7861.924-2.709
S2380-0.408-29.0913.779.160.67-0.4241.870-2.294
SW40740.17312.3513.1915.681.190.1821.865-1.682
W2938-0.225-16.0714.1611.310.80-0.2351.825-2.060
NW2738-0.206-14.6812.9510.540.81-0.2151.924-2.139

According to Table 1, the relation between landslides and slope gradient shows that most of the landslides were observed for slopes >30°. This means that there exists a good correlation between slope angle and the occurrence of landslides. Counter to the slope, there is no specific correlation between the elevation and the occurrence of landslides. For the slope aspect, the most susceptible classes are N and SW. Regarding the stream density factor, the most suitable class is 0.8–1. In the case of NDVI factor, the susceptibility increases gradually with the decrease in NDVI values. The relation between the lithology and landslide inventory of the study area shows that landslides occurred mainly in (i) Mio-Pliocene and Numidian clay, marl of the Tellian unit and clayey marl of Priabonian, current superficial deposits and colluvium and (ii) the flysh of Numidian unit. Concerning rainfall factor, the classes greater than 550 mm are the most susceptibles. The relation between landslide occurrences and distance to lineaments and streams shows that a large number of the landslides were observed in the area with a distance less than 400 m and 200 m, respectively.

3.2 Landslide susceptibility mapping

The produced LSMs of the three methods (Fig. 5a–c) were subdivided into five hierarchic classes (very low, low, moderate, high and very high) using the NB classifier. The very high susceptibility zones are located in the NW and SE of the study area spreading out 22% (IV), 19% (WoE) and 9% (FR) of the total area. The moderate susceptibility has a close distribution with 21.59% (IV), 25.17% (WoE) and 29.49% (FR) of the total area. Finally, the low to very low susceptibility is spread in the N, NE and SW of the study area within the average of 24.1% (IV), 27% (WoE) and 39.35% (FR).

In order to verify quantitatively the consistency of the models, the landslide density (LD) for each class was calculated. The LD values reach 3.09 (IV), 3.35 (FR) and 3.38 (WoE) in very high and high susceptibility zones. This confirms the proneness to landsliding in these areas when compared to those of moderate, low and very low susceptibility. The very low susceptible area has the lowest values of LD with 0.0323 (IV), 0.04 (FR) and 0.02 (WoE). According to Table 2, it can be explained that there is a gradual decrease in LD from the high to the low susceptible area.

Table 2

LD in different susceptibility zones of the study area (IV, WoE, FR)..

SusceptibilityAreaLandslideareaLD
km2%km2%
IV
Very low14.8176.580.0150.210.0323
Low39.45717.520.1241.720.0981
Moderate48.60921.590.6268.650.4007
High72.91232.381.55321.460.6627
Very high49.36221.924.91767.963.0998
FR
Very low27.48112.210.0320.440.0363
Low61.11627.140.6028.330.3068
Moderate66.39829.490.73510.160.3446
High49.89622.165.37274.253.3504
Very high20.2669.000.4936.820.7577
WoE
Very low19.6698.740.0150.210.0243
Low41.12318.260.0971.330.0730
Moderate56.67625.170.3935.430.2158
High63.92728.391.97227.260.9602
Very high43.76219.444.75865.763.3834
Figure 5
Figure 5

LSMs for a: IV, b: WoE, and c: FR. d: ROC curves of the three used models.

Citation: Studia Geotechnica et Mechanica 40, 3; 10.2478/sgem-2018-0024

3.3 Validation procedure

In order to test the compatibility of the models and to determine their prediction ability, the AUC (success and prediction rates) was plotted. The prediction rates of the FR and WoE methods are in the range of 0.7332–0.7907, indicating a good performance of the models, with a slight differentiation for the WoE model. In the case of the success rates, the IV method shows a low score of 0.5881. This value is less convincing than the two remaining models (Fig. 5d).

3.4 Discussion

From these calculations, we can say that the slope gradient is the most important parameter for slope stability analysis, because the driving force of mass movement increases with increasing slope. In the study area, the gradients over than 25° in soft rocks and/or soils are the most susceptible to landsliding. Because of their physical behaviour, the clay, marl and colluvium formations are more prone to shear stress and therefore most susceptible to landslide occurrence. It is clear that elevation factor influences less the landslide occurrence, against to other factors. This is probably due to the accumulation of weathered materials on low altitudes (corresponding to gentle slopes) and hardness of outcropping formations in high altitudes. The streams network is a further controlling parameters of landslide occurrence, as streams’ erosion may undercut the foot of the slopes and saturate their lower part. Moreover, for the fault proximity, we cannot extract a clear correlation between the nearness to faults and the landslide occurrence. The relation between landslide occurrence and NDVI shows that most of the landslides occur in areas with low values. The relation between aspect and landslide occurrences shows that most of the landslides were observed in areas with N and SW-facing slopes. This is probably due to the more exposure to rainfall coming from these sides.

4 Conclusions

In the last few years, many qualitative and quantitative methods have proved their worth in landslide susceptibility assessment. In this study, three statistical approaches were proposed to promote the advantages and overcome the shortcomings of GIS-based methods in assessing the susceptibility of landslides in the east of Constantine province, constantly threatened by this phenomenon. IV, WoE and FR are efficiently used to analyse the correlation between landslide occurrence and their conditioning factors. Initially, a single event-based landslide inventory was established and was then randomly divided into training and validation datasets. Nine causative factors were adopted for the landslide susceptibility analysis such as slope gradient, slope aspect, elevation, distance to streams, lithology, distance to lineaments, precipitation, NDVI and stream density. Weights and class indexes were attributed to each of the associated factors. The three LSMs were categorized: namely, “very high, high, moderate, low, and very low” using NB classifier. The resulted maps have been validated by comparison with known landslide locations. The WoE presents the higher prediction rate (79.07%), distinguished as the most performant model for the landslide susceptibility assessment in the study area. The results show that the slope gradient, rainfall, lithology and distance to streams are the most influencing factors in landslide occurrence according to their associated weights. The obtained LSMs provide helpful tools for the decision-makers and engineers. They may assist as good beneficial guides for planners in the scope of choosing appropriate locations for the implementation of developments.

Acknowledgements

All authors would like to thank the International Association of Water Resources in the Southern Mediterranean Basin for its help in the editing of the manuscript. The authors are also grateful to the anonymous reviewers for their valuable corrections in the article.

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    Bourenane H. Bouhadad Y. Guettouche M. Braham M. (2014). 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 74(2) 337-355.

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    Pradhan B. Lee S. (2010). Delineation of landslide hazard areas on Penang Island Malaysia by using frequency ratio logistic regression and artificial neural network models. Environmental Earth Sciences 60(5) 1037-1054.

    • Crossref
    • Export Citation
  • [22]

    Yalcin A. Reis S. Aydinoglu A.C. Yomralioglu T. (2011). A GIS-based comparative study of frequency ratio analytical hierarchy process bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon NE Turkey. Catena 85(3) 274-287.

    • Crossref
    • Export Citation
  • [23]

    Karim Z. Hadji R. Hamed Y. (2018). GIS-based approaches for the landslide susceptibility prediction in Setif Region (NE Algeria). Geotechnical and Geological Engineering 1-16.

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    Mahdadi F. Boumezbeur A. Hadji R. Kanungo D. P. Zahri F. (2018). GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province NE Algeria. Arabian Journal of Geosciences 11(17) 476.

    • Crossref
    • Export Citation
  • [25]

    Hadji R. Limani Y. Demdoum A. (2014). Using multivariate approach and GIS applications to predict slope instability hazard case study of Machrouha municipality NE Algeria. In Information and Communication Technologies for Disaster Management (ICT-DM) 2014 1st International Conference on IEEE pp. 1-10.

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    • Crossref
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    Demdoum A. Hamed Y. Feki M. Hadji R. Djebbar M. (2015). Multi-tracer investigation of groundwater in El Eulma Basin (northwestern Algeria) North Africa. Arabian Journal of Geosciences 8(5) 3321-3333.

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    • Export Citation
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    Hamed Y. Ahmadi R. Hadji R. Mokadem N. Dhia H. B. Ali W. (2014). Groundwater evolution of the continental intercalaire aquifer of Southern Tunisia and a part of Southern Algeria: Use of geochemical and isotopic indicators. Desalination and Water Treatment 52(10-12) 1990-1996.

    • Crossref
    • Export Citation
  • [29]

    Yilmaz I. (2009). Landslide susceptibility mapping using frequency ratio logistic regression artificial neural networks and their comparison: A case study from Kat landslides (Tokat— Turkey). Computers & Geosciences 35(6) 1125-1138.

    • Crossref
    • Export Citation
  • [30]

    Tseng C.M. Lin C.W. Hsieh W.D. (2015). Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories. Natural Hazards & Earth System Sciences Discussions 3(2) 1137-1173.

    • Crossref
    • Export Citation
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    Hadji R. Rais K. Gadri L. Chouabi A. Hamed Y. (2017). Slope failure characteristics and slope movement susceptibility assessment using GIS in a medium scale: a case study from Ouled Driss and Machroha municipalities Northeast Algeria. Arabian Journal for Science and Engineering 42(1) 281-300.

    • Crossref
    • Export Citation
  • [32]

    Hadji R. Limani Y. Boumazbeur A.E. Demdoum A. Zighmi K. Zahri F. et al. (2014). Climate change and its influence on shrinkage–swelling clays susceptibility in a semi-arid zone: a case study of Souk Ahras municipality NE-Algeria. Desalination and Water Treatment 52(10-12) 2057-2072.

    • Crossref
    • Export Citation
  • [33]

    Hadji R. Boumazbeur A. Limani Y. Baghem M. el Madjid Chouabi A. Demdoum A. (2013). Geologic topographic and climatic controls in landslide hazard assessment using GIS modeling: a case study of Souk Ahras region NE Algeria. Quaternary International 302 224-237.

    • Crossref
    • Export Citation
  • [34]

    Hamed Y. Redhaounia B. Sâad A. Hadji R. Zahri F. Zighmi K. (2017). Hydrothermal waters from karst aquifer: Case study of the Trozza basin (Central Tunisia). Journal of Tethys 5(1) 33-44.

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    Guzzetti F. Reichenbach P. Cardinali M. Galli M. and Ardizzone F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1-4) 272-299.

    • Crossref
    • Export Citation
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    El Mekki A. Hadji R. Fehdi C. (2018) Use of slope failures inventory and climatic data for landslide susceptibility vulnerability and risk mapping in Souk Ahras region. Mining Science 24 237-249

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    Besser H. Mokadem N. Redhaounia B. Hadji R. Hamad A. Hamed Y. (2018). Groundwater mixing and geochemical assessment of low-enthalpy resources in the geothermal field of southwestern Tunisia. Euro-Mediterranean Journal for Environmental Integration 3(1) 16.

    • Crossref
    • Export Citation
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    Dahoua L. Yakovitch S.V. Hadji R. Farid Z. (2017). Landslide susceptibility mapping using analytic hierarchy process method in BBA-Bouira Region case study of east-west highway NE Algeria. In Euro-Mediterranean Conference for Environmental Integration Springer Cham pp. 1837-1840.

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    Hamad A. Hadji R. Bâali F. Houda B. Redhaounia B. Zighmi K. et al. (2018). Conceptual model for karstic aquifers by combined analysis of GIS chemical thermal and isotopic tools in Tuniso-Algerian transboundary basin. Arabian Journal of Geosciences 11(15) 409.

    • Crossref
    • Export Citation
  • [40]

    Althuwaynee O.F. Pradhan B. Lee S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences 44 120-135.

    • Crossref
    • Export Citation
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    Lee S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing 7 1477-1491.

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    Pardeshi S.D. Autade S.E. Pardeshi S.S. (2013). Landslide hazard assessment: recent trends and techniques Springer Plus 2 253.

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    Pradhan B. Oh H.J. Buchroithner M. (2010). Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomatics Natural Hazards and Risk 1(3) 199-223.

    • Crossref
    • Export Citation
  • [44]

    Lee S. Choi J. Min K. (2004). Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun Korea. International Journal of Remote Sensing 25(11) 2037-2052.

    • Crossref
    • Export Citation
  • [45]

    Cevik E. Topal T. (2003). GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline Hendek (Turkey). Environmental Geology 44(8) 949-962.

    • Crossref
    • Export Citation
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    Van Westen C.J. (1993). Application of geographic information systems to landslide hazard zonation. ITC Publication vol. 15. International Institute for Aerospace and Earth Resources Survey Enschede p. 245.

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    Hamed Y. Hadji R. Redhaounia B. Zighmi K. Bâali F. El Gayar A. (2018). Climate impact on surface and groundwater in North Africa: a global synthesis of findings and recommendations. Euro-Mediterranean Journal for Environmental Integration 3(1) 25.

    • Crossref
    • Export Citation

If the inline PDF is not rendering correctly, you can download the PDF file here.

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    Hadji R. Chouabi A. Gadri L. Raïs K. Hamed Y. Boumazbeur A. (2016). Application of linear indexing model and GIS techniques for the slope movement susceptibility modeling in Bousselam upstream basin Northeast Algeria. Arabian Journal of Geosciences 9(3) 192.

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    Dahoua L. Yakovitch S.V. Hadji R.H. (2017). GIS-based technic for roadside-slope stability assessment: An bivariate approach for A1 East-West highway North Algeria. Mining Science 24 117-127.

  • [20]

    Bourenane H. Bouhadad Y. Guettouche M. Braham M. (2014). 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 74(2) 337-355.

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    Pradhan B. Lee S. (2010). Delineation of landslide hazard areas on Penang Island Malaysia by using frequency ratio logistic regression and artificial neural network models. Environmental Earth Sciences 60(5) 1037-1054.

    • Crossref
    • Export Citation
  • [22]

    Yalcin A. Reis S. Aydinoglu A.C. Yomralioglu T. (2011). A GIS-based comparative study of frequency ratio analytical hierarchy process bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon NE Turkey. Catena 85(3) 274-287.

    • Crossref
    • Export Citation
  • [23]

    Karim Z. Hadji R. Hamed Y. (2018). GIS-based approaches for the landslide susceptibility prediction in Setif Region (NE Algeria). Geotechnical and Geological Engineering 1-16.

  • [24]

    Mahdadi F. Boumezbeur A. Hadji R. Kanungo D. P. Zahri F. (2018). GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province NE Algeria. Arabian Journal of Geosciences 11(17) 476.

    • Crossref
    • Export Citation
  • [25]

    Hadji R. Limani Y. Demdoum A. (2014). Using multivariate approach and GIS applications to predict slope instability hazard case study of Machrouha municipality NE Algeria. In Information and Communication Technologies for Disaster Management (ICT-DM) 2014 1st International Conference on IEEE pp. 1-10.

  • [26]

    Hamad A. Baali F. Hadji R. Zerrouki H. Besser H. Mokadem N. et al. (2018). Hydrogeochemical characterization of water mineralization in Tebessa-Kasserine karst system (Tuniso-Algerian Transboundry basin). Euro-Mediterranean Journal for Environmental Integration 3(1) 7.

    • Crossref
    • Export Citation
  • [27]

    Demdoum A. Hamed Y. Feki M. Hadji R. Djebbar M. (2015). Multi-tracer investigation of groundwater in El Eulma Basin (northwestern Algeria) North Africa. Arabian Journal of Geosciences 8(5) 3321-3333.

    • Crossref
    • Export Citation
  • [28]

    Hamed Y. Ahmadi R. Hadji R. Mokadem N. Dhia H. B. Ali W. (2014). Groundwater evolution of the continental intercalaire aquifer of Southern Tunisia and a part of Southern Algeria: Use of geochemical and isotopic indicators. Desalination and Water Treatment 52(10-12) 1990-1996.

    • Crossref
    • Export Citation
  • [29]

    Yilmaz I. (2009). Landslide susceptibility mapping using frequency ratio logistic regression artificial neural networks and their comparison: A case study from Kat landslides (Tokat— Turkey). Computers & Geosciences 35(6) 1125-1138.

    • Crossref
    • Export Citation
  • [30]

    Tseng C.M. Lin C.W. Hsieh W.D. (2015). Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories. Natural Hazards & Earth System Sciences Discussions 3(2) 1137-1173.

    • Crossref
    • Export Citation
  • [31]

    Hadji R. Rais K. Gadri L. Chouabi A. Hamed Y. (2017). Slope failure characteristics and slope movement susceptibility assessment using GIS in a medium scale: a case study from Ouled Driss and Machroha municipalities Northeast Algeria. Arabian Journal for Science and Engineering 42(1) 281-300.

    • Crossref
    • Export Citation
  • [32]

    Hadji R. Limani Y. Boumazbeur A.E. Demdoum A. Zighmi K. Zahri F. et al. (2014). Climate change and its influence on shrinkage–swelling clays susceptibility in a semi-arid zone: a case study of Souk Ahras municipality NE-Algeria. Desalination and Water Treatment 52(10-12) 2057-2072.

    • Crossref
    • Export Citation
  • [33]

    Hadji R. Boumazbeur A. Limani Y. Baghem M. el Madjid Chouabi A. Demdoum A. (2013). Geologic topographic and climatic controls in landslide hazard assessment using GIS modeling: a case study of Souk Ahras region NE Algeria. Quaternary International 302 224-237.

    • Crossref
    • Export Citation
  • [34]

    Hamed Y. Redhaounia B. Sâad A. Hadji R. Zahri F. Zighmi K. (2017). Hydrothermal waters from karst aquifer: Case study of the Trozza basin (Central Tunisia). Journal of Tethys 5(1) 33-44.

  • [35]

    Guzzetti F. Reichenbach P. Cardinali M. Galli M. and Ardizzone F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1-4) 272-299.

    • Crossref
    • Export Citation
  • [36]

    El Mekki A. Hadji R. Fehdi C. (2018) Use of slope failures inventory and climatic data for landslide susceptibility vulnerability and risk mapping in Souk Ahras region. Mining Science 24 237-249

  • [37]

    Besser H. Mokadem N. Redhaounia B. Hadji R. Hamad A. Hamed Y. (2018). Groundwater mixing and geochemical assessment of low-enthalpy resources in the geothermal field of southwestern Tunisia. Euro-Mediterranean Journal for Environmental Integration 3(1) 16.

    • Crossref
    • Export Citation
  • [38]

    Dahoua L. Yakovitch S.V. Hadji R. Farid Z. (2017). Landslide susceptibility mapping using analytic hierarchy process method in BBA-Bouira Region case study of east-west highway NE Algeria. In Euro-Mediterranean Conference for Environmental Integration Springer Cham pp. 1837-1840.

  • [39]

    Hamad A. Hadji R. Bâali F. Houda B. Redhaounia B. Zighmi K. et al. (2018). Conceptual model for karstic aquifers by combined analysis of GIS chemical thermal and isotopic tools in Tuniso-Algerian transboundary basin. Arabian Journal of Geosciences 11(15) 409.

    • Crossref
    • Export Citation
  • [40]

    Althuwaynee O.F. Pradhan B. Lee S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences 44 120-135.

    • Crossref
    • Export Citation
  • [41]

    Lee S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing 7 1477-1491.

  • [42]

    Pardeshi S.D. Autade S.E. Pardeshi S.S. (2013). Landslide hazard assessment: recent trends and techniques Springer Plus 2 253.

  • [43]

    Pradhan B. Oh H.J. Buchroithner M. (2010). Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomatics Natural Hazards and Risk 1(3) 199-223.

    • Crossref
    • Export Citation
  • [44]

    Lee S. Choi J. Min K. (2004). Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun Korea. International Journal of Remote Sensing 25(11) 2037-2052.

    • Crossref
    • Export Citation
  • [45]

    Cevik E. Topal T. (2003). GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline Hendek (Turkey). Environmental Geology 44(8) 949-962.

    • Crossref
    • Export Citation
  • [46]

    Van Westen C.J. (1993). Application of geographic information systems to landslide hazard zonation. ITC Publication vol. 15. International Institute for Aerospace and Earth Resources Survey Enschede p. 245.

  • [47]

    Hamed Y. Hadji R. Redhaounia B. Zighmi K. Bâali F. El Gayar A. (2018). Climate impact on surface and groundwater in North Africa: a global synthesis of findings and recommendations. Euro-Mediterranean Journal for Environmental Integration 3(1) 25.

    • Crossref
    • Export Citation
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Journal information
Impact Factor


CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.213
Source Normalized Impact per Paper (SNIP) 2018: 1.106

Figures
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    Geological map of the study area.

  • View in gallery

    Optical remote sensing images and air photo used for the landslides determination in the study area: a- Panchromatic satellite image. b- A landslide occurred in highway’s segment before construction (air photo). c- A neighboring road affected by a rotational landslide.

  • View in gallery

    Landslide inventory map of the study area.

  • View in gallery

    Landslide conditioning factor used for landslide susceptibility mapping in the study area: a- Lithology. b- Slope angle. c- Slope aspect. d- Elevation.

  • View in gallery

    LSMs for a: IV, b: WoE, and c: FR. d: ROC curves of the three used models.

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