# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 4 Conclusions  
To address the issues of ignoring parameter uncertainty in earth-rock dam compaction quality evaluation and the limitations in prediction accuracy and generalization of traditional compaction quality evaluation models, a prediction model for the compaction quality of earth-rock dams based on IFA-RF is proposed. A predictive analysis and performance comparison study was conducted using actual compaction data from a southwestern earth-rock dam project. The following conclusions were drawn:  
1. Uncertainty Analysis: An uncertainty analysis method was applied to scientifically and rationally analyze the influencing parameters of compaction quality. The uncertainty was quantified, and by considering the parameter uncertainty, the evaluation results for the earth-rock dam compaction quality were made more comprehensive and precise.
2. IFA: An improved firefly algorithm, coupled with DIW, adaptive factors, and DES, was proposed. DIW allowed the search process to gradually shift from broad exploration to local optimization. A sine function was used to adjust the F parameter in the differential evolution strategy, enhancing the algorithm's adaptability. Additionally, differential mutation and crossover strategies were introduced in each iteration to improve the population diversity and enhance the global search capability. Compared to the traditional FA, the proposed IFA demonstrated superior accuracy, effectiveness, and robustness when optimizing four benchmark functions.
3. Compaction Quality Prediction Model: A compaction quality prediction model based on the IFA-RF was developed. The algorithm adaptively optimized the _Ntree_ and _Mtry_ parameters of the RF, creating a model that revealed the complex nonlinear relationships between input factors, such as compaction parameters, material source  
Figure 3: The curve of the noise variance intensity with the accuracy of different models.  
parameters, and meteorological parameters, and the compaction quality. The model's predictive performance was further improved. Verification through an engineering case study showed that, compared to prediction models based on traditional RF, BPNNs, and MLR, the proposed model exhibited superior accuracy.  
W.L. (Corresponding Author): Contributed to the conceptualization and methodology design, conducted data analysis, and participated in manuscript drafting and revision; Y.Y.: Assisted in data collection and validation, provided technical support for experiments, and contributed to data interpretation; P.X.: Supervised the overall project, coordinated the research activities, contributed to the conceptualization, and revised the manuscript critically for important intellectual content.; X.Z.: Performed data curation and visualization and contributed to manuscript writing and editing; Y.Z.: Contributed to the formal analysis, validation, and technical review of the study. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Nos. 52471275 and U23A20663) and the Fuzhou University Research Start up Fund (XRC-24071).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Authors [PERSON] and [PERSON] are employed by the Fujian Provincial Water Resources and Hydropower Survey and Design Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interests.