## 4 Results
### Run-off Risk Assessment (FFPI)
#### 4.3.2 Application of the MLP Model for FFPI Computation  
The first step in MLP training and in calculating FFPI\({}_{1989}\) and FFPI\({}_{2019}\) was done through the determination of \(FR\) coefficients. For 1989, the highest \(FR\) values were obtained by the following categories of land use/land cover: fruit trees (\(FR\) = 3.46), water bodies (\(FR\) = 2.45), shrub (\(FR\) = 2.21), and built-up areas (\(FR\) = 2.13). Hydrological soil group D (\(FR\) = 2.1) and slopes between 15 and 25\({}^{\circ}\) (\(FR\) = 1.98) also obtained high \(FR\) coefficients. For 2019, the highest \(FR\) values were achieved by the following class/categories of land use/land cover and lithology: water bodies (\(FR\) = 3.7), shrubs (\(FR\) = 3.16), sandstone and conglomerates (\(FR\) = 3), sandstone and tuffs (\(FR\) = 2.79), sandstone and marks (\(FR\) = 2.79), and slopes between 25 and 45\({}^{\circ}\) (\(FR\) = 2.72) (Table 4). The RMSE for 1989 was equal to 0.0183, while for 2019, the RMSE was equal to 0.0157. Both values denote a very good performance for the MLP algorithm. Furthermore, through MLP, the importance of each flash-flood predictor was estimated for each of the two years. Thus, for 1989, the most important factor was slope angle (0.373), followed by land use/land cover\({}_{1989}\) (0.287), lithology (0.146), MFI\({}_{1989}\) (0.122), TWI (0.104), TPI (0.097), profile curvature (0.059), aspect (0.051), convergence index (0.05), and hydrological soil groups (0.043). In can be noted that for 2019, the slope angle also had the highest importance (0.404), followed by MFI\({}_{2019}\) (0.289), land use/land cover (0.236), TWI (0.194), lithology (0.128), TPI (0.103), convergence index (0.084), hydrological soil group (0.067), profile curvature (0.045), and aspect (0.032). Through the multiplication of MLP weights with the \(FR\) normalized coefficient, the FFPI values were obtained. The highest FFPI values were concentrated in the eastern part of the Zabala catchment for both 1989 and 2019 (Figures 7(a) and 7(b), respectively). Several areas with a high FFPI occurred in the western part of the study area, on high-altitude pastures, having a declivity higher than 15\({}^{\circ}\) and developed on soils with a clay texture. For 1989, the high and very high FFPI values covered approximately 34% of the total area (Figure 7(a)), while in 2019, they represented 46% of the Zabala catchment (Figure 7(b)). Intermediate values for FFPI for both years were evenly distributed across 29-30% of the total area. The low and very low values of FFPI\({}_{1989}\), ranging from 0.1 to 0.31, were associated with 35% of the catchment, while the same values of FFPI\({}_{2019}\) were distributed over 24% of the Zabala river basin. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Flood Predictor** & **AM\({}_{1989}\)** & **AM\({}_{2019}\)** \\ \hline Slope & 0.87 & 0.91 \\ TPI & 0.28 & 0.39 \\ TWI & 0.61 & 0.46 \\ Land use/land cover & 0.47 & 0.73 \\ Lithology & 0.52 & 0.59 \\ Profile curvature & 0.41 & 0.22 \\ Aspect & 0.17 & 0.13 \\ Convergence index & 0.35 & 0.32 \\ Hydrological soil groups & 0.23 & 0.26 \\ MFI & 0.68 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Predictive ability of flash-flood predictors. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Factor** & **Class** & \(\textbf{FR}_{\textbf{1998}}\) & \( \begin{array}{c}\textbf{FR}_{\textbf{1989}}\\ \textbf{N}\end{array} \) & \(\textbf{FR}_{\textbf{2019}}\) & \( \begin{array}{c}\textbf{FR}_{\textbf{2019}}\\ \textbf{N}\end{array}