# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 6 Conclusions  
1. The Elman model and the PCA-Elman model achieve prediction accuracies of 74.28% and 88.57%, respectively, for debris flow risk. Utilizing PCA to mitigate the intercorrelation among evaluation indicators enhances the accuracy of the Elman model. The PCA-Elman model demonstrates higher AUC and AP values compared to the PCA-RF, PCA-SVM, and PCA-BP models. The accuracy of assessing debris flow risk using the Elman Neural Network surpasses that of RF, SVM, and BP models. Furthermore, employing the ADASYN algorithm to augment data effectively addresses the issue of data imbalance in debris flow samples. As validated through the examples in this paper, the predictive accuracy of the PCA-Elman model can be increased to 98.33%.
2. The maximum relief, watershed incision density, and the reserve of loose solid materials are crucial indicators for the genesis of debris flows in Yunnan Province, significantly influencing the prediction outcomes of debris flow hazards. Their values are relatively close across different levels of debris flow risk, which can impact the model's assessment of debris flow risk. These factors warrant particular attention when developing predictive models and disaster prevention strategies.
3. Incorporating samples from unknown regions into the trained model significantly mitigates spatial variability and enhances the model's predictive accuracy for these areas. The PCA-Elman model demonstrates superior generalization capabilities compared to the PCA-BP, PCA-SVM, and PCA-RF models when dealing with unknown debris flow samples.  
While this study successfully incorporated sample data from the Bailongjiang River basin to validate the model's generalization capabilities, the acquisition of debris flow sample data is limited, resulting in a relatively small sample size that may constrain further verification of the model's generalization capabilities. Despite the limitations in sample size, the potential of the data was maximized through meticulously designed experiments and data analysis methods to ensure the stability, adaptability, and effectiveness of the model's results. Future research could consider increasing the sample size, particularly in regions with varying geological conditions and climatic characteristics, to further enhance the model's generalization capabilities and predictive stability.  
Conceptualization, S.C., Y.Y., X.S. and A.Z.; methodology, S.C. and X.S.; validation, S.C., X.S. and M.Z.; formal analysis, S.C., X.S., M.Z. and N.H.; investigation, S.C., Y.Y., X.S., A.Z. and W.Z.; writing--original draft preparation, S.C. and X.S.; writing--review and editing, S.C., Y.Y., X.S., M.Z. and A.Z.; visualization, S.C., X.S., M.Z. and N.H.; project administration, S.C., Y.Y. and X.S.; funding acquisition, A.Z. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This study was jointly supported by the Central Government Guided Local Science and Technology Development Fund Project (basic research of free exploration type) (No. 246Z5405G), Hebei Natural Science Foundation (No. F2024403007), Science Research Project of Hebei Education Department (BJK2024090), and Hebei GEO University Technology Innovation Team Project (No. KJCXTD-2021-08).  
Not applicable.  
The data presented in this study are available on request from the author.  
The authors declare no conflicts of interest.