# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 4 Results
### Analysis of Misclassification in Prediction Results  
The prediction results indicate that misclassifications primarily occur between Category 1 and Category 2. The misclassification of Category 1 as Category 2 may be attributed to the scarcity of training samples, with only 2 out of 37 samples belonging to Category 1. The rarity of Category 1 in the training set likely prevents the model from learning sufficiently to accurately distinguish between these categories. The misclassification of Category 2 as Category 3 may stem from the similarity in values for maximum watershed height difference, watershed incision density, and loose solid material reserves. The subtle differences in these data points may make it challenging for the model to discern the boundary between Category 2 and Category 3, leading to classification errors. To investigate the impact of the aforementioned evaluation indicators on the risk of debris flows, Grey Relation Analysis (GRA) was employed to analyze the correlation between them. GRA measures the degree of relevance between two entities within a system; the greater the relevance, the more similar the trends of the corresponding sequences. In other words, the higher the correlation of the evaluation indicators, the greater their impact on the risk of debris flows [25]. Using IBM SPSS Statistics 29 software, the original evaluation indicators were treated as characteristic sequence variables, while the debris flow risk levels were considered the reference sequence variables to calculate and analyze the correlation between the evaluation indicators and the debris flow risk levels. Since the original evaluation indicator data lacked a clear trend, they were first normalized to a dimensionless mean before calculation. A discrimination coefficient of 0.5 was used, and upon running the IBM SPSS Statistics 29 software, the correlation between the evaluation indicators and debris flow risk was ultimately obtained, as shown in (Table 5). S8 (loose solid material reserves) had the highest correlation of 0.958, followed by S5 (maximum relative height difference in the watershed) with 0.944, and S6 (watershed incision density) ranked fourth with 0.940. This indicates that Categories 2 and 3 have similar values in terms of maximum watershed height difference, watershed incision density, and loose solid material reserves, posing significant challenges for the model in discerning the risk of debris flows. In addition to the proximity of evaluation indicator values affecting the model's prediction of debris flow risk, the imbalance of samples is also a significant cause of model misjudgment. Acquiring debris flow data is challenging and costly, and in practice, the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Indicators** & **Correlation Degree** & **Ranking** \\ \hline S1 & 0.883 & 8 \\ S2 & 0.862 & 10 \\ S3 & 0.890 & 7 \\ S4 & 0.943 & 3 \\ S5 & 0.944 & 2 \\ S6 & 0.940 & 4 \\ S7 & 0.870 & 9 \\ S8 & 0.958 & 1 \\ S9 & 0.908 & 6 \\ S10 & 0.918 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation results. imbalance of risk samples in the obtained debris flow data is very severe. According to existing research experience, eliminating sample imbalance can significantly improve the model's predictive results [26]. Therefore, it is necessary to perform data balancing on the original debris flow data. Data balancing techniques are generally categorized into three types: oversampling, undersampling, and mixed sampling. The oversampling method balances the dataset by increasing the number of minority class samples, while undersampling does the opposite by reducing the number of majority class samples. Mixed sampling combines both approaches [27]. Given the difficulty and cost associated with acquiring debris flow data, this study employs the Adaptive Synthetic Sampling (ADASYN) algorithm.