**3.1 Analytical maps**

The production of five analytical maps (**Figure 3**) was necessary to obtain the erosion sensitivity map.

Once the maps were produced, we used experts' assessment to compare in a reciprocal matrix, the different parameters of soil predisposition to erosion. The weights expressed by the comparison matrix (**Table 7**) were validated by the consistency of judgment in accordance with the equation … . and appended to the various maps produced.

According to the judgments made at the level of the comparison matrix, it is noted that the soil cover plays the most important role in the occurrence of erosion.

**Figure 3.** *Predisposition parameters of the cartographic model.*


#### **Table 7.**

*Comparison of weights relative to factors of sensitivity.*

*Modeling of Soil Sensitivity to Erosion Using the Analytic Hierarchical Process: A Study… DOI: http://dx.doi.org/10.5772/intechopen.111742*

According to experts consulted, the effectiveness of a downpour depends on the degree of protection that the plant cover can provide to preserve the soil. This led them to consider the role of the land use factor with a score of 44%. Secondly, the slope factor contributes about 28% to the occurrence of erosion. Indeed, runoff acts when the slope becomes steeper on soft soils.

However, on the steep slopes of Bamboutos Mountains, the technique of cultivation by plowing, which is the most widespread, helps to crumble the soil into fine particles that are easily transported by runoff water along steep slopes. For these experts, the structural stability that determines the soil's susceptibility to erosion depends on organic matter, aggregation, and texture. The result of the weighting obtained displays a score of 15% for the soil type factor. In general, the sensitivity to erosion in the Nkam watershed from the pedological point of view increases from moderately organic hydromorphic soils to humic ferralitic soils on basalt and shows on the whole that these soils are less resistant to erosion. The triggering factor precipitation (5%), which is the sinequanone condition for water erosion precedes that of drainage density (8%) that conditions diffuse erosion and generates the formation of capping crusts by reducing soil infiltration capacity.

#### **3.2 Mapping of soil sensitivity to erosion**

Once the final matrix was produced, the weights (ω) expressed by the matrix were associated with each thematic map in GIS using reclassification functions. Then, the cartographic algebra operations were carried out by applying Eq. (4). The usual formats for these calculations are rasters. As such, the resultant is a soil erosion sensitivity map for the Menoua watershed (**Figure 4**).

**Figure 4.** *Potential sensitivity of the Menoua watershed to erosion.*

The analysis of **Figure 2** and the statistical distribution of the zoning of soil sensitivity to erosion in the Menoua watershed shows that levels of very strong and strong erosion sensitivity represent 8.82% or 5592.26 hectares of the total area. Field observations show that these classes of erosion are characterized by constantly plowed bare ferralitic soils on steep slopes, highly perceptible in the northern (toward the summit of the Bamboutos Mountains) and southern (on the Foréké escarpment which separates the Bamileke plateau from the Mbo plain) parts of the watershed. The moderate erosion sensitivity level covers 4781.31 hectares or 7.55% of the basin and is explained by the coincidence of medium slope classes with areas with a reduced vegetation cover, especially in the north, west, and southeastern parts of the watershed. Finally, the class of low and very low erosion status of 52981.91 hectares (i.e., 7% of the study area) occupies the center of the basin. Here, erosion is less advanced due to a fairly large vegetation cover and limited influence of the slope gradient.

#### **3.3 Model validation**

Any model must be validated so that the mapping of risks does not lead to risks for the mapping system [27]. Since no previous survey of the study area has relied on quantitative methods, this makes confirmation of results difficult. The model presented (**Figure 3**) was validated thanks to a reconnaissance mission of the mapped sites. Therefore, the validation of this model required field measurements and observations.

GPS surveys of certain bare slopes were carried out. It appears from this mission that all the sites mapped do not fully reflect the reality on the ground. For each test value, the two information plans (GPS surveys/model) were superimposed using GIS. Some sites identified as prone to erosion represent a rate of 73.87% coincidence with the model. Moreover, for about 26.13% of the results obtained, the model did not agree and sometimes very largely with field measurements (**Figure 5**). Sites classified as very sensitive are those on which we can either carry out reforestation schemes or no-till farming techniques.
