# 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 terms of correlation coefficients, the correlation between the prediction results and the measured values exceeds 0.7 for all four models, indicating a strong correlation between the predicted and actual values. The 95% confidence interval for IFA-RF is [0.862, 1.041],  
Figure 1: Line graph of the measured values versus IFA-RF model predictions. which does not include zero and has a relatively narrow range. The t-value of 21.087 is significantly greater than the critical value of 2, further supporting the significant positive impact of IFA-RF on the dependent variable and validating its statistical significance. The statistical results further confirm the significance of all the models: RF ([0.461, 0.837], \(p\) = 0.000), MLR ([0.098, 0.622], \(p\) = 0.007), and BPNN ([0.090, 0.356], \(p\) = 0.001). Significance testing reveals that the results of all four models are statistically significant at the 0.01 level. Based on these findings, the IFA-RF algorithm model and traditional compaction quality prediction models show consistency in predicting the compaction quality. Superior Performance: In terms of prediction accuracy, the IFA-RF model yields prediction results with average values closest to the measured average, followed by the BPNN, RF, and MLR models, in decreasing order of similarity. Regarding standard deviation, the BPNN's prediction results are closest to the measured values. This is due to the higher uncertainty inherent in BPNN as a \"black box\" fitting model, which results in a larger standard deviation, despite its poor prediction accuracy. In contrast, the linear regression method, which computes prediction results using formulae, exhibits low dispersion and, consequently, the smallest standard deviation. However, the IFA-RF model not only matches the measured values well in terms of trend but also achieves the highest accuracy. Additionally, the IFA-RF model outperforms the RF, BPNN, and MLR models in terms of MSE and MAE, and it attains a higher correlation coefficient than all three. Overall, the IFA-RF-based compaction quality prediction model demonstrates superior performance, exhibiting the closest trend to the measured values, minimal error, and the best correlation. Additionally, the robustness of the proposed IFA-RF model was tested using noisy data. The model's robustness is characterized by the decline in accuracy as noise is added to the data. Random noise values following a normal distribution were added to the training dataset, and the noise variance was adjusted to control its magnitude. The decline in model accuracy with increasing noise intensity was then evaluated to assess the model's noise resistance. The noise levels were calibrated based on preliminary field data, so that the variance and distribution of the noise approximate the measurement errors and environmental fluctuations typically observed in operational conditions. Although the noise model is relatively simplistic--employing independent random perturbations--it is intended to mimic the typical uncertainties encountered in practice. The complexity of the construction environment for hydraulic engineering core-wall rockfill dams inevitably introduces noisy data into the compaction operation information. Therefore, the stronger the model's robustness, the more effective it is for accurately predicting the spatial compaction quality of the dam. Figure 3 illustrates the decrease in the Pearson correlation coefficient (R) of different models as the noise intensity (\(\sigma\)) increases. As shown in Figure 3, the BPNN model exhibits the poorest noise resistance, with its accuracy rapidly declining as the noise intensity increases. The RF and MLR models, while more resistant to noise than the BPNN model,  
Figure 2: Diagram of the accuracy analysis of the prediction results from different prediction models. also show a significant decline in accuracy, especially the MLR model, which experiences a sharp drop in accuracy when the noise intensity reaches 0.9.