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The aim of this work is to map the susceptibility of sites to landslides. To assess the susceptibility of the zone, GIS techniques were used. Susceptibility factors are selected and split into two groups: active and passive factors. Passive factors regroup all the intrinsic conditions existing on the field at all times. The active factors or triggering factors are present sporadically and are added to the passive factors to trigger a landslide. With the weighted overlay method using
*ArcGIS*
^{©}, four scenarios have been developed. A first scenario where only passive factors are combined and three scenarios for which we have for each scenario the passive factors combined with an active factor. With these different scenarios, five levels of susceptibility are obtained in the zone. These levels range from very low to very high susceptibility. For the different scenarios, the results show that the zone consists mainly of very low to low susceptibility with at least 61% of the area, followed by moderate susceptibility (23.54% to 38.24%) and last land with high susceptibility to very high with less than 1% of the surface. Fields with high to very high susceptibility are located on the slopes of the hills. Among the active factors, only the rainfall significantly modifies the percentage of land susceptible to landslide but remains in the field of moderate susceptibility. The predicted susceptibilities are closer to the observed landslides around the Thies Cliff than to the Dias Horst.

The Thies region is rich in mineral and hydrogeological resources that contribute to the economic development of the country and require good management [

Landslides studies carried out in Senegal focus mainly on the Dakar coasts [

The area, located in western Senegal, covers the geological domains of Dias Horst and Thies Cliff (

In terms of geology, the area is part of the Senegal-Mauritanian-Guinean Sedimentary Basin [

We have the sandstones and clays of the Dome of Dias distributed in Paki Formation of Campanian age and in Formation of the Cap de Naze dated Maastrichtian. The limestones of Ndayane-Poponguine-Bandia are dated to the Danian. The Ypresian corresponding to the Thies Formation is represented by the laminated clays, the marls of Ravin des Voleurs, the marls of Lam Lam, the limestones and phosphate clays of Pallo, the limestone and calcareous marl of Bellevue and Mt Rolland and the sandstone of Lam-Lam. The limestones of the Plateau of Bargny, attached to the formation of Bargny are dated Lutetien [

The mapping of susceptibility is a key component of preventing landslides. Several methods have been proposed by different authors. At present, there is no unified method for assessing susceptibility and producing risk maps [

On the basis of previous work [

Qualitative methods are based exclusively on the judgment of the person responsible for assessing susceptibility. These methods, also known as expert assessment approaches [

For the synthesis based on overlay or combination of index maps, the expert selects and maps the factors that affect slope stability and, based on personal experience, assigns to each weighted value that is proportionate to its expected relative contribution in generating failure.

Assigning weight on a subjective basis to the many factors that govern slope stability is the main limitation of qualitative methods.

Quantitative methods use different approaches: statistical analysis, geotechnical engineering approaches and the neural network analysis.

・ Statistical analysis compare the spatial distribution of landslides with the parameters considered. In the bivariate statistical analysis, each factor is compared to the landslide map. The weights assigned to each class of each parameter are determined on the basis of landslide density for in each individual class. The bivariate statistical approach is widely used by geologists and many parameters can be considered: lithology, slope angle, height of slope, land use [

・ Geotechnical methods may be deterministic or probabilistic. Deterministic approaches involve analyzing specific engineering sites or embankments. The main physical properties are quantified and applied to specific mathematical models and the safety coefficient is calculated. These models are commonly used in soil mechanics for slope stability studies. In probabilistic approaches, basic geotechnical models are maintained but the variability of material properties is taken into account.

・ Neural network analysis consists of selecting input parameters for the different neurons and assigning weights at the connections. These weights are in turn summed and the output obtained is compared with the expected output, which makes it possible to determine the error. The procedure proceeds iteratively until convergence.

The current trend of assessments of landslides favors the use of quantitative methods specifically based on GIS [

In this study, GIS techniques are used to map the risk of landslides, combining several factors of instability. Two-variable statistical analysis, which is a quantitative method, has also been used. The landslides encountered in the area are listed and their geographical coordinates recorded. Although these coordinates do not reflect the nature or magnitude of the phenomenon, they still allow the map of landslides to be generated in points [

Landslides can be triggered by a variety of external stimuli, such as heavy rainfall, earthquakes, fluctuations in the level of groundwater, storm waves etc. These stimuli are at the origin of a rapid increase in the stress or the decrease in the shear strength of the materials forming the slope.

The choice of instability factors plays an important role in the accuracy of stability modeling results [

In this study, we added to these factors the proximity of the road and the land use.

The slopes and the hydrographic network are extracted from the ASTER imagery (ASTER DEM October 2011). Lineaments were extracted from the band 7 of the Landsat 8 (OLI) images to which we applied a 3 × 3 enhancement Laplacian filter and Sobel 7 × 7 directional filters [

The pre-treatment is carried out under Erdas^{©}. From the pre-processed images, the digitization of the lineaments was done under ArcMap^{©}.

The density of lineaments and the density of hydrographic network were generated from the ArcGIS^{©} Line Density module. Land us is mapped from Landsat 8 (OLI) colored compositions.

Factors are split into two groups: passive and active (

Each factor is classified into five levels of susceptibility ranging from very low

Factor | Rank | Susceptibility levels | Weight |
---|---|---|---|

Passive factors | |||

Slopes (˚) | 11 - 31 | Very high | 5 |

6 - 11 | High | 4 | |

3 - 6 | Moderate | 3 | |

1 - 3 | Low | 2 | |

0 - 1 | Very low | 1 | |

Lineaments (km of lineaments per km²) | 2.03 - 3.46 | Very high | 5 |

1.47 - 2.03 | High | 4 | |

0.94 - 1.47 | Moderate | 3 | |

0.35 - 0.94 | Low | 2 | |

0 - 0.35 | Very low | 1 | |

Lithology | Shell Sands and Vases | Very high | 5 |

Clays and sands | High | 4 | |

Marl | Moderate | 3 | |

Limestone | Low | 2 | |

Volcanic rocks | Very low | 1 | |

Land use | Quarries | Very high | 5 |

Buildings | High | 4 | |

Waters bodies | Moderate | 3 | |

Bare floors | Low | 2 | |

Vegetation | Very low | 1 | |

Active Factors | |||

Rainfall (mm) | 620 - 660 | Very high | 5 |

590 - 620 | High | 4 | |

560 - 590 | Moderate | 3 | |

530 - 560 | Low | 2 | |

500 - 530 | Very low | 1 | |

Hydrographic network (km/km²) | 0.825 - 1.031 | Very high | 5 |

0.618 - 0.825 | High | 4 | |

0.412 - 0.618 | Moderate | 3 | |

0.206 - 0.412 | Low | 2 | |

0 - 0.206 | Very low | 1 | |

Road network (Proximity in meters) | 0 - 100 | Very high | 5 |

100 - 200 | High | 4 | |

200 - 300 | Moderate | 3 | |

300 - 400 | Low | 2 | |

400 - 500 | Very low | 1 |

to very high [

In order to weigh the factors, a spatial correlation between observed land movements and factors (passives and actives) was performed. The correlation coefficient is used to determine the influence factor according to the following relationship Equation (1):

I_{i} is the influence of the I^{th} factor, N is the total number of factors and R² is the correlation coefficient. The weighting of each factor will be defined by the following relation Equation (2):

The integration of factors for field susceptibility mapping was done in two stages. In the first step, the passive factors are combined to produce a basic map that describes the initial conditions (scenario 1). In the second step, the base map is combined with each active factor to define scenarios 2, 3 and 4. We note, however, that the list of scenarios is not exhaustive and other scenarios could be considered.

The correlations between landslides and passive factors are presented in

The hydrographic network is the active factor with the best correlation. Watercourses are essentially temporary and the density of the river system depends on the rainy season which lasts at most 4 months. We also considered the high density of the hydrographic network, which results in significant infiltration, thus leading to an increase in pore water pressures.

The active factors are combined one by one with the passive factors defining the different scenarios. The influence of the active factor is determined according to the scenario.

The different steps of the methodology used are summarized in the following diagram (

The integration of the factors involved in each scenario is achieved using the ArcGIS^{©} Weighted Overlay module from the corresponding maps sorted and converted to the 10-meter resolution raster format.

It should be noted that Weighted Overlay supports only integer weights. Thus, the Pi represented in

The results of the mapping are represented in Figures 4-7 and correspond to the

Passives Factors | R² | Significant R | I_{i} | P_{i} |
---|---|---|---|---|

Slopes | 0.9989 | 0.8900 | 22.25 | 30.17 |

Lithology | 0.9976 | 0.7600 | 19 | 25.76 |

Land use | 0.9972 | 0.7200 | 18 | 24.41 |

Lineaments | 0.9958 | 0.5800 | 14.5 | 19.66 |

100.00 |

Scenario 2 | ||||
---|---|---|---|---|

Factors | R² | Significant R | I_{i} | P_{i} |

Slopes | 0.9989 | 0.8900 | 17.8 | 24.05 |

Lithology | 0.9976 | 0.7600 | 15.2 | 20.54 |

Land use | 0.9972 | 0.7200 | 14.4 | 19.46 |

Lineaments | 0.9958 | 0.5800 | 11.6 | 15.68 |

Hydrographic Network | 0.9975 | 0.7500 | 15 | 20.27 |

Scenario 3 | ||||

Factors | R² | Significant R | I_{i} | P_{i} |

Slopes | 0.9989 | 0.8900 | 17.8 | 28.53 |

Lithology | 0.9976 | 0.7600 | 15.2 | 24.36 |

Land use | 0.9972 | 0.7200 | 14.4 | 23.08 |

Lineaments | 0.9958 | 0.5800 | 11.6 | 18.59 |

Rainfall | 0.9917 | 0.1700 | 3.4 | 5.45 |

Scenario 4 | ||||

Factors | R² | Significant R | I_{i} | P_{i} |

Slopes | 0.9989 | 0.8900 | 17.8 | 25.50 |

Lithology | 0.9976 | 0.7600 | 15.2 | 21.78 |

Land use | 0.9972 | 0.7200 | 14.4 | 20.63 |

Lineaments | 0.9958 | 0.5800 | 11.6 | 16.62 |

Proximity to roads | 0.9954 | 0.5400 | 10.8 | 15.47 |

integration of the passive factors (

The combination of the passive factors, without involving the active factors, makes it possible to distinguish in the zone five (05) levels of susceptibility going from “very low” to “very high”. Lands with high to very high susceptibility account for about 1% of the area. These lands are located mainly at the slopes of the Thies Cliff and the Dias Horst (

By combining passive factors with the hydrographic network (scenario 2 and

The combination of the passive factors with the rainfall makes it possible to distinguish in the zone five levels of susceptibility (scenario 3 and

For Scenarios 2, 3 and 4, we noted that more than 90% of the study area consists of low to moderate susceptibility. Lands with high to very high susceptibility represent less than 1% (

By combining passive factors and proximity to the road network (scenario 4 and

For the various scenarios, the details of the percentages of land as a function of the susceptibility class are given in

The combination of the passive factors with the different active factors shows significant variations in the proportion of low and moderate susceptibility sites. The results show lower proportions for soils with very low or high susceptibility.

Analysis of scenarios involving active factors shows that rainfall is the most influential factor that significantly increases the proportion of moderate susceptibility sites.

The area as a whole consists of stable to very stable soils, but the occurrence of significant precipitation can alter this state of equilibrium and cause local instabilities in certain slopes.

For validation, the models corresponding to the different scenarios are correlated

Susceptibility classes | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 |
---|---|---|---|---|

Very low | 2.01 | 1.36 | 0.71 | 2.31 |

Low | 64.25 | 71.56 | 60.56 | 73.97 |

Moderate | 32.75 | 26.72 | 38.24 | 23.54 |

High | 0.56 | 0.34 | 0.48 | 0.18 |

Very high | 0.43 | 0.02 | 0.01 |

with the landslides map. The ArcGIS proximity tool, was used to compare the distances between the positions of the predicted susceptibilities and the observed landslides. The results are presented in the histograms below (

Passive factors alone (

・ a first population containing 70% of the sample at distances of less than 1000 m and

・ a second population comprising 30% of individuals with distances greater than 1000 m.

Passive factors combined with rainfall (

By combining passive factors with proximity to the road network, 33% of predicted susceptibilities are localized within 400 m of the observed landslides, 55% between 600 and 1400 m and 12% between 1800 and 2000 m (

In all cases, the predicted susceptibilities are closer to the observed landslides around the Thies Cliff than to the Dias Horst.

The predicted susceptibilities by combining the passive factors and the hydrographic network are always located at distances greater than 2200 m from the observed movements. This situation is due to the weak influence of the hydrographic network on susceptibility (

This work shows that the zones of the Dias Horst and the Thies Cliff while remaining globally stable present locally areas susceptible to instabilities. The susceptibility factors classified as passive factors and active factors allowed us to generate four models of susceptibility. Apart from Scenario 4 (passive factors combined with proximity to the road network), all other models subdivide the area into five levels of susceptibility. For the model obtained with Scenario 4, no very high level of susceptibility was noted. For all scenarios, we noted the predominance of low to very low susceptibility sites. This could be explained by the low relief. Rainfall is the active factor that most influences susceptibility. High to very high susceptibility sites occupy less than 1% of the area and are encountered at the Thies Cliff and some slopes of the Dias Horst hills. The hydrographic network, although considered as an active factor, does not seem to influence the susceptibility to the landslides in our study area. For all our models, the prediction is better around the Thies Cliff. In a later study, it would be interesting to spatialize the active factors or to weigh them according to their geographical positions. Moreover, the development of a software allowing to take into account the decimal part of the scores should improve the results obtained.

The authors would like to thank Drs. Ibrahima MALL (Department of Geology- Cheikh Anta DIOP University of Dakar) and Makhaly BA (UFR Engineering Sciences-Thies University) for their valuable contributions and advice in this research project.

Ndoye, I., Ndiaye, M., Sarr, D., Faye, P.S. and Cissé, I.K. (2017) Cartography of Landslide Susceptibility around the Dias Horst and Thies Cliff- Senegal. International Journal of Geosci- ences, 8, 821-836. https://doi.org/10.4236/ijg.2017.86047