# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 5 Results and Discussion
### Validation  
Conventional soil classification methods rely on a thorough examination of various soil parameters, including coefficients of curvature and uniformity, PI, organic or inorganic soil classification, LL, percentage passing No. 4 sieve, and percentage passing No. 200 sieve, totaling seven factors. However, practical limitations such as time constraints, budgetary restrictions, or data availability may impede the acquisition of all seven parameters. Therefore, this study investigates whether the RF model can effectively evaluate soil properties even when some input soil parameters are unavailable. Its aim is to assess the model's ability to compute performance indices and accurately identify soil properties in such scenarios. This study encompasses eight cases, where Case 1 corresponds to scenarios with no missing factors, indicating the utilization of the complete set of seven factors as input variables. Cases 2 to 8, conversely, involve the absence of one factor each. A summary detailing the missing factors for each case is presented in Table 4. In this study, nine performance indices, VAF, R\({}^{2}\), PI, NS, WI, AIC, RMSE, MAE, and WMAPE values, were employed to calculate the scores, with the highest accuracy cases receiving the highest scores and vice versa. This study initially calculated nine performance indices for each of the eight cases. The performance index values for the eight cases are summarized in Table 4.  
This study further calculates scores for these eight cases based on nine performance indices, ranks them, and assigns scores to each case. Table 5 lists the results of scores across eight cases. The calculation of scores is explained as follows: scores ranged from eight points for the top-performing case (such as Case 1, achieving the highest R\({}^{2}\) score) to eight points for the least-performing case (Case 8). The remaining cases were scored in descending order according to their respective error metric results. Each case was evaluated \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Missing** & **R\({}^{2}\)** & **RMSE** & **VAF** & **PI** & **MAE** & **WI** & **WMAPE** & **NS** & **AIC** \\ \hline
1 & NA & 1 & \(7.96\times 10^{-4}\) & 100 & 2.00 & \(6.48\times 10^{-6}\) & 1 & \(1.16\times 10^{-5}\) & 1 & \(-2.24\times 10^{7}\) \\ \hline
2 & Factor 1 & 1 & \(6.36\times 10^{-3}\) & 99.97 & 1.99 & \(1.46\times 10^{-3}\) & 1 & \(2.63\times 10^{-3}\) & 1 & \(-1.58\times 10^{7}\) \\ \hline
3 & Factor 2 & 1 & \(4.41\times 10^{-3}\) & 99.98 & 2.00 & \(7.03\times 10^{-4}\) & 1 & \(1.27\times 10^{-3}\) & 1 & \(-1.70\times 10^{7}\) \\ \hline
4 & Factor 3 & 0.99 & \(3.14\times 10^{-2}\) & 99.15 & 1.95 & \(2.55\times 10^{-2}\) & 1 & \(4.58\times 10^{-2}\) & 0.99 & \(-1.08\times 10^{7}\) \\ \hline
5 & Factor 4 & 0.99 & \(2.97\times 10^{-2}\) & 99.24 & 1.96 & \(2.10\times 10^{-2}\) & 1 & \(3.79\times 10^{-2}\) & 0.99 & \(-1.10\times 10^{7}\) \\ \hline
6 & Factor 5 & 0.98 & \(4.67\times 10^{-2}\) & 98.1 & 1.92 & \(2.62\times 10^{-2}\) & 1 & \(4.73\times 10^{-2}\) & 0.98 & \(-9.58\times 10^{6}\) \\ \hline
7 & Factor 6 & 0.82 & \(1.45\times 10^{-1}\) & 77.79 & 1.45 & \(1.21\times 10^{-1}\) & 0.95 & \(2.18\times 10^{-1}\) & 0.78 & \(-6.03\times 10^{6}\) \\ \hline
8 & Factor 7 & 0.09 & \(3.26\times 10^{-1}\) & 0.69 & 0.77 & \(2.83\times 10^{-1}\) & 0.4 & \(5.11\times 10^{-1}\) & 0.54 & \(-3.50\times 10^{6}\) \\ \hline \hline \end{tabular}  
* Notation: Factor 1 is C, Factor 2 is Cu, Factor 3 is PI, Factor 4 is organic soil or inorganic soil, Factor 5 is LL, Factor 6 is percentage passing No. 4 sieve, and Factor 7 is percentage passing No. 200 sieve.  
\end{table} 
Table 4: Performance index values across eight cases.