# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 3 Results
### Comparison and Discussion of Compaction Quality Prediction Models  
In the comparison and discussion of compaction quality prediction models, error metrics are commonly used as indicators to assess the fitting performance of the model. The mean squared error (MSE) and normalized mean squared error (NMSE) are used as average error metrics, reflecting the overall deviation of the model's predicted values from the observed values. Smaller values of these metrics indicate better model performance. Additionally, the mean absolute error (MAE) measures the average absolute deviation between individual observations and the arithmetic mean, while the Pearson correlation coefficient (R) is used to assess the linear relationship between two random variables. In this study, the evaluation of the compaction quality prediction method not only considers the error analysis and comparison among the IFA-RF, RF, BPNN, and MLR models but also incorporates the mean values and standard deviations, thereby providing a more comprehensive comparison of model performance. Through error analysis, the MSE, MAE, and R for the IFA-RF model's predicted dry density values were calculated and compared with the observed values. This evaluation assesses the effectiveness of the IFA-RF algorithm in predicting the compaction quality \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Material Source Parameters**} & \multicolumn{4}{c}{**Compaction Parameters**} & \multicolumn{4}{c}{**Meteorological Parameters**} \\ \cline{2-10}  & P5 & **Moisture Content** & **Curvature Coefficient** & **Uniformity Coefficient** & **Compaction Thickness** & **Static Collection Collection Cycles** & **Total Collection Cycles** & **Total Collection** & **Total Collection** \\ \hline Entropy value & 0.8766 & 0.7760 & 0.7109 & 0.6755 & 0.5082 & 0.5915 & 0.3093 & 0.6409 & 0.8569 & 0.9093 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Entropy values of the compaction quality influence parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group (\%)** & **38.2–40.2** & **40.2–42.2** & **42.2–44.2** & **43.42–46.2** & **46.2–48.2** \\ \hline Count (n) & 9 & 34 & 37 & 11 & 9 \\ \hline Frequency & 0.09 & 0.34 & 0.37 & 0.11 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Frequency distribution table of the _P5_ content. of earth-rock dams. The results indicate that the significance test was below the 0.05 significance level, and the correlation coefficient between the actual and predicted values was 0.90107, suggesting a significant correlation. This implies that the predicted results effectively reflect the actual values, confirming that the proposed model is suitable for predicting compaction quality in earth-rock dams. Furthermore, the MAE for the IFA-RF model's predictions was 0.00609 and the MSE was 6.02 \(\times\) 10\({}^{-5}\), indicating high prediction accuracy. To validate the consistency and superiority of the proposed IFA-RF algorithm, a comparative analysis was conducted of the performance of the IFA-RF, RF, BP neural network (BPNN), and multiple linear regression (MLR) models in practical engineering applications. The prediction results of the four models were compared and analyzed (see Figure 2). In the BPNN configuration, the hidden layer comprised 10 neurons. The training process was configured to run for a maximum of 10,000 iterations with a learning rate of 0.00001, and training could be terminated early once the target error fell below 0.000004. In the RF model, the number of trees was fixed at 500, and at each split, the optimal division was selected from only two candidate features. The analysis of Figure 2 leads to the following conclusions. Accuracy Analysis: The analysis reveals that the relative error and mean squared error (MSE) of the four compaction quality prediction models are consistent in terms of accuracy levels, while the accuracy levels for the mean absolute error (MAE) are nearly identical.