## 4 Discussion  
Determining effective parameters in GE and providing a GESM are the first steps in risk management. In regards to this, prediction of areas susceptible to erosion is associated with uncertainty, various models can be used to predict it accurately. Over the past decades, numerous statistical and empirical models have been developed to predict environmental hazards, such as GE, by various researchers around the world [12; 14; 28; 30; 31; 45]. Due to some of the limitations of the aforementioned models such as time consuming, complexity, costly, and need a lot of data, in recent years data mining methods have been presented. Data mining is a process of discovery of relationships, patterns, and trends that consider the vast amount of information stored in databases with template recognition technology [51; 58; 59]. The most important applications of data mining are categorization, estimation, forecasting, group dependency, clustering, and descriptions. The results of data mining models show that in RF, BRT, and MARS mode, distance from roads had the highest impact in the occurrence of gully erosion in the study area. This result is in line with [10; 49]. If the engineering measures \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Susceptibility** \\ **Classes** \\ \end{tabular} } & \multicolumn{2}{c}{**Total Area of Classes**} & \multicolumn{2}{c}{**Gully in Classes**} & \multicolumn{2}{c}{**No Gully**} & \multicolumn{2}{c}{**Seed**} \\  & & **Area (km)** & **\%** & **Area (km)** & **\%** & **Area (km)** & **Cell (\%)** & **SCAI** \\ \hline \multirow{6}{*}{RF} & Very Low & 525.97 & 62.03 & 0.01 & 0.86 & 525.96 & 0.01 & 61.08 \\  & Low & 148.28 & 17.49 & 0.04 & 5.67 & 148.24 & 0.24 & 0.74 \\  & Moderate & 79.42 & 9.37 & 0.11 & 14.80 & 79.31 & 1.15 & 0.08 \\  & High & 56.34 & 6.64 & 0.16 & 21.95 & 56.18 & 2.41 & 0.03 \\  & Very High & 37.88 & 4.47 & 0.41 & 56.72 & 37.46 & 9.27 & 0.00 \\ \hline \multirow{6}{*}{MARS} & Very Low & 339.01 & 39.98 & 0.02 & 2.10 & 339.00 & 0.04 & 10.45 \\  & Low & 194.83 & 22.98 & 0.01 & 1.48 & 194.82 & 0.05 & 4.89 \\  & Moderate & 131.17 & 15.47 & 0.04 & 5.67 & 131.13 & 0.27 & 0.58 \\  & High & 77.35 & 9.12 & 0.08 & 11.59 & 77.26 & 0.93 & 0.10 \\  & Very High & 105.50 & 12.44 & 0.58 & 79.16 & 104.92 & 4.64 & 0.03 \\ \hline \multirow{6}{*}{BRT} & Very Low & 605.37 & 71.40 & 0.04 & 5.55 & 605.33 & 0.06 & 12.59 \\  & Low & 88.38 & 10.42 & 0.03 & 4.56 & 88.34 & 0.32 & 0.33 \\ \cline{1-1}  & Moderate & 52.01 & 6.13 & 0.06 & 8.26 & 51.95 & 0.98 & 0.06 \\ \cline{1-1}  & High & 34.13 & 4.03 & 0.08 & 11.34 & 34.05 & 2.06 & 0.02 \\ \cline{1-1}  & Very High & 67.98 & 8.02 & 0.51 & 70.28 & 67.46 & 6.40 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Seed cell area index (SCAI) values in RF, multivariate adaptive regression spline (MARS), and boosted regression tree (BRT) data mining models. Figure 7: Seed cell area index (SCAI) values for different susceptibility classes in RF, MARS, and BRT data mining models. are not considered in site selection and construction of roads as anthropogenic structures in nature, they can act as a causative factor in environmental hazards such as landslide and gully erosion. The construction of roads in bare lands with erosion-sensitive formations has led to the expansion of gully erosion in the study area, so that the construction of a road without proper culverts causes disrupted of natural drainage and runoff concentrations, thus eroding the bare lands and resulting in the formation of a gully. The results of the validation of data mining models showed that the RF model more accurately predicted areas that are sensitive to gully erosion. These results are consistent with the results of [36; 43; 46; 59], which introduced the RF model as a strong and high-performance model. One of the most widely used data mining methods is the RF model.