## 5 Conclusions  
This study achieves effective reconstruction of seepage pressure monitoring data through an integrated approach combining improved particle filtering for multi-type anomaly detection and MissForest imputation for data correction, validated by case studies and comparative experiments. The key conclusions are as follows:  
* The proposed method can accurately identify multiple types of anomalies (including random errors, systematic biases, and gradual drifts) in dam seepage pressure monitoring data, while the MissForest imputation algorithm effectively fills missing values with data that conform to the characteristic patterns of seepage pressure behavior.
* The analysis of practical examples demonstrates that the proposed method eliminates the need for manual setting of outlier detection criteria. By combining \(m_{k}\) and \(J_{k}\), it can accurately identify the types of outliers while also determining their magnitude and location. This provides a novel solution to the limitation of conventional methods, which can only detect a single type of outlier and fail to effectively determine multiple types of outliers.
* The experimental comparison results demonstrate that the proposed method achieves better reconstruction performance for anomalous data than the other three methods. The improved particle filter exhibits significantly better detection performance than the traditional particle filter, with RMSE and MAE reduced by 34.6% and 51.0%, respectively. Meanwhile, the MissForest imputation method outperforms mean imputation, reducing RMSE and MAE by 18.8% and 34.7%, respectively.
* The proposed method effectively addresses the limitations of conventional detection approaches in dam safety assessment and seepage pressure anomaly identification, such as offline detection, low efficiency, strong subjectivity in evaluation, and the inability to accurately identify multiple types of outliers. Additionally, it enables effective reconstruction of seepage pressure data, providing reliable data support for seepage state assessment.  
Methodology, software, conceptualization, validation, writing--original draft preparation, K.F.; formal analysis, writing--review, supervision, funding acquisition, C.Y.; software, validation, L.P.; conceptualization, writing--review, J.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the 2024 Graduate Research and Innovation Program of Xinjiang Agricultural University (XIAUGRI2024015), the Graduate Education Reform Project of Xinjiang Agricultural University (xjaualk-yjs-2018005).  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors gratefully acknowledge the support from the 2024 Graduate Research and Innovation Program of Xinjiang Agricultural University (XIAUGRI2024015), the Graduate Education Reform Project of Xinjiang Agricultural University (xjaualk-yjs-2018005), and the author thanks Xinjiang Agricultural University for providing research practice platform.  
**Conflicts of Interest:** The authors have no relevant financial or non-financial interests to disclose.