# Mapping of Kill Erosion of the Middle Volga (Russia) Region Using Deep Neural Network
## 4 Results and Discussion  
The trained RECNN (rill erosion convolutional neural network) was tested on an independent test dataset. The recognition accuracy was 0.62, F1-measure was 0.76, and loss-function was 0.27. Qualitatively analyzing the obtained evaluation results (Figure 5), we can note a relatively high level of recognition of the rill erosion network. It is crucial that not a single case of detection of gullies or ground roads, which are abundant in the study area, instead of rills, was recorded. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline AREA & Basin area (sq. km) \\ SLOPE & Average slope of the basin (degrees) \\ HMEAN & Average elevation in the basin (m) \\ HMAXMIN & Elevation range in the basin (m) \\ TMEAN & Mean annual air temperature (degrees C) in the basin \\ TMAX & Mean annual maximum temperature (degrees C) in the basin \\ TMIN & Mean annual minimums temperature (degrees C) in the basin \\ TAMP & Mean annual variation of air temperature (degrees C) in the basin \\ T1 MEAN & Mean air temperature in January (deg. C) in the basin \\ TAKT & Sum of active temperatures (degrees C) in the basin \\ RMEAN & Mean annual precipitation in the basin (mm) \\ RSS & Mean May-August precipitation (mm) in the basin \\ RCOLD & Mean precipitation for the cold period of the year (mm) in the basin \\ RWARM & Mean precipitation for warm period of the year (mm) in the basin \\ RVC & Mean annual precipitation variation coefficient (\%) in the basin \\ GTK & Mean value of the hydrothermal coefficient in the basin \\ PARENT1 & Predominant type of soil-forming rocks \\ SOIL0 & Predominant soil type \\ LES\_PROC & Forest cover of the basin (\%) \\ LAND\_COD & Landscape subtype \\ PLT\_RANGE & Population density (people/square km) \\ ANTR1 & Anthropogenic load (score) \\ X & Longitude of the basin centroid \\ Y & Latitude of the basin centroid \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Natural–anthropogenic factors of rill erosion selected for analysis [60]. The trained and tested model was decided to apply to the entire study area (Figure 6). For the obtained geometry, the length was calculated, which was aggregated to a grid of basins [60] (Figure 7). Analyzing the obtained maps, the presence of specific clusters becomes evident. The first cluster--the highest density of fill erosion--is located in the left-bank part of the Volga River in the Saratov region. The second cluster--the area of medium erosion--characterizes the right bank of the Volga in the Saratov region and Zakamye of the Republic of Tatarstan. The Republics of Mari El and Chuvashia territories are characterized mainly by low fill erosion, forming the third cluster. Figure 5: An example of the results of applying RECNN to a test dataset. Figure 6: Results of fill erosion recognition over the entire study area (**A**) and enlarged area with recognized streams in Sentinel 2 image (**B**). Figure 7: Map of fill erosion density. Quartiles calculate intervals. In total, information in the study area is aggregated on 3037 basins with an average area of 117.83 sq. km. The average density of rill erosion over the entire study area is 0.23 km/sq. km, and the maximum density is 3.79 km/sq. km. In general, the distribution of the total length of rill erosion in the study area is lognormal; that is, most of the measurements contain, in general, relatively small values of the total length (Figure 8). For most of the selected factors, no strong correlations were found with the total length of rill erosion. Only some factors revealed statistically significant (\(p<0.001\)) weak relationships (r > 1.0.31). For example, an inverse correlation describes the relationship between total rill erosion length and forest cover, hydrothermal coefficient, mean annual precipitation, mean May-August precipitation, and mean warm-season precipitation. The inverse relationship with forest cover is more than obvious and only states a well-known fact--the less forest cover of the territory, the greater the risk of soil and gully erosion.