# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
## 5 Conclusions and Future Work  
In this study, a novel interval prediction model based on the Bi-LSTM network and SCQP algorithm is proposed to explain the uncertainties in dam deformation prediction. With the aid of DL technology and interval prediction paradigm, the Bi-SCQLSTM can construct the deformation PIs to capture and quantify the inherent uncertainties associated with the structural behavior of the dam. The comparison experiments on the deformation monitoring dataset from a real concrete arch dam fully validated the applicability and superiority of the proposed interval model. The general conclusions of this study are summarized as follows:  
1. Dam deformation data from an arch dam are selected to test the performance of the proposed Bi-SCQLSTM. The experiment results show that, compared with the current mainstream interval prediction methods, the Bi-SCQLSTM can construct deformation PIs that meet high-quality standard.
2. Compared to ML algorithms and unidirectional DL neural networks, the Bi-LSTM also maintains a significant advantage in interval prediction with the help of hidden layer information in different time dimensions and a larger memory threshold.
3. A reasonable interval prediction paradigm can reduce the prediction error due to deformation uncertainties to some extent. As a result, point prediction results transformed from PIs have higher accuracy than standard point prediction model.  
Overall, the proposed Bi-SCQLSTM provides an effective scientific monitoring approach for the safe operation and maintenance of dams. By utilizing high-quality deformation PIs, the impact of uncertainty in dam deformation during specific periods can be observed. Practitioners can gain a better understanding of the potential trends in dam deformation, allowing them to develop more targeted operational strategies. These strategies can be further refined based on the degree to which uncertainty characteristics influence deformation at different times, thereby optimizing dam safety management and operational efficiency. However, it is worth acknowledging that despite its outstanding performance in dam deformation prediction, the proposed method has notable limitations. First, the model's effectiveness needs to be thoroughly validated across a broader range  
Figure 13: Quantitative evaluation of the point prediction performance of different models.  
of engineering cases. Second, exploring how interval prediction can be leveraged for the effective early warning of anomalous dam deformations remains a key focus of future research. Finally, the computational efficiency of the proposed model needs to be further improved.  
[PERSON]: Contributed to the conceptualization and methodology design, conducted data analysis, and participated in manuscript drafting and revision; J.F.: Assisted in data collection and validation, provided technical support for experiments, and contributed to data interpretation; W.L. (Corresponding Author): Supervised the overall project, coordinated the research activities, contributed to the conceptualization, and revised the manuscript critically for important intellectual content; [PERSON]: Responsible for conducting the experiments and preparing the original draft of the manuscript; [PERSON]: Performed data curation and visualization and contributed to manuscript writing and editing; [PERSON]: 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 funded by the National Natural Science Foundation of China (No. 42301002 and 52109118).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author acknowledges the technical and financial support provided by the National Natural Science Foundation of China (Grant No. 42301002, and 52109118).  
The authors declare no conflict of interest.