## 8 Predictive Model Development  
During model development, three primary algorithms were employed, and the results of their predictions for each target were calculated using a variety of neurons and hidden layer selections. In addition, the algorithm calculation was conducted three times for each approach with various numbers of hidden layer neurons. For each referenced HWE, the optimal outcomes of each algorithm were computed. Using evaluation indices, the abilities  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Prediction Case 1–1 (Field Recorded Data) & & & Target \\ \hline \hline Rainfall & Soil Matric Suction & Soil Temperature & Air Temperature & Soil Moisture Content \\ \hline Prediction Case 1–2 (Field Recorded Data) & & & Target \\ \hline \hline Rainfall & Soil Moisture Content & Soil Temperature & Air Temperature & Soil Nitric Suction \\ \hline Prediction Case 1–3 (Field Recorded Data) & & & Target \\ \hline \hline Rainfall & Soil Matric Suction & Soil Moisture Content & Air Temperature & Soil Temperature \\ \hline Prediction Case 2 (Numerical Analyzed Data) & & & Target \\ \hline \hline Rainfall & Soil Matric Suction & Soil Moisture Content & Soil Displacement & Factor of Safety \\ \hline Prediction Case 3 (Combined for 2-4H:1V Slope Ratio) & & & Target \\ \hline \hline Rainfall & Soil Matric Suction & Soil Moisture Content & Soil Displacement & Factor of Safety \\ \hline Soil Temperature & & Air Temperature & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Schematic representation of predictive model structure targeting various parameters based on both different field and numerical datasets.  
Figure 13: Schematic representation of ANN model development and modeling procedure-based flowchart of the proposed hybrid model using PSO-ANN and BOA-ANN.  
of each model were compared. For all algorithms, the highest number of neurons yielded the best results. Figures 14-17 present the developed predictive model results for SMS and FS for selected depth and HWEs (Table 3) for ANN-BR, PSO-ANN, and BOA-ANN ML-based techniques.  
Figure 15: Developed predictive model (ANN-BR, PSO-ANN, and BOA-ANN) for in situ soil parameter (SMS) at 3 m depth over time for **(a)** HWE 1, **(b)** HWE 2, **(c)** HWE 3, **(d)** HWE 4, **(e)** HWE 5, and **(f)** HWE 6 (cases 1 and 2 in Table 3 classification).  
Figure 16: Developed predictive model (ANN-BR, PSO-ANN, and BOA-ANN) for in situ soil parameter (SMS) at 5 m depth over time for (**a**) HWE 1, (**b**) HWE 2, (**c**) HWE 3, (**d**) HWE 4, (**e**) HWE 5, and (**f**) HWE 6 (cases 1 and 2 in Table 3 classification).  
Several performance indices, including coefficient of determination (R2), mean square error (MSE), variance accounted for (VAF), and regression error characteristic (REC), were also computed to assess the accuracy of the models' outcome predictions. During the development of the model, the performance of each algorithm was compared using indices such as R2, MSE, VAF, and \(\delta\) (accuracy predictor). Next, the algorithms' REC performance was evaluated (Figure 18). Eventually, sensitivity analysis revealed how each input variable  
Figure 17: Developed predictive model (ANN-BR) for FS for different HWE slope ratios: (**a**) 2H:1V, (**b**) 3H:1V, and (**c**) 4H:1V (case 3 in Table 3 classification).  
influenced each model objective (Figure 19). The results of all developed models for the six HWEs, including their variations for different depths (1.5 m, 3 m, and 4.5 m), tabulated performance indices, REC performance curves, and sensitivity analysis, are reported in [2; 35; 36].  
Figure 18: REC curve for the three developed predictive models (ANN-BR, PSO-ANN, and BOA-ANN) targeting SMS, along with the impact of the number of iterations on the predictive model responses for HWE 1 at 1.5 m depth.