# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 3 Data Analysis and Processing
### Data Analysis  
To rapidly grasp the distribution and probability density of the debris flow raw data, violin plots for each evaluation indicator were created (Figure 4), which intuitively reflect the distribution and probability density of the debris flow hazard assessment indicators. Each violin plot consists of three parts: a central boxplot, whiskers extending upwards and downwards, and density plots on both sides. The boxplot displays the median, quartiles, and outliers of the data, with the upper and lower bounds of the box representing the third and first quartiles, respectively, and the line inside the box indicating the median and mean values of the data. The whiskers extend to the maximum and minimum values of the data, with black and white dots representing the mean and median values, respectively. The density plot depicts the peaks of the data distribution, with greater width indicating higher data density. Observations from (Figure 4) reveal that the data for evaluation indicators S1 and S2 are primarily concentrated within a specific range below 500, indicating that the majority of debris flow events are small in scale, although there are occasional large-scale events. In the analysis of the three evaluation indicators S4, S5, and S6, violin plots highlight the diversity and complexity of topographical conditions within the debris flow dataset. The distribution of S4 data shows that the main channel lengths are generally short, likely associated with small-scale debris flow events, but there are also individual values indicating that debris flows can occur in longer channels under certain conditions. Data for S5 and S6 are distributed across all ranges, with records of debris flows occurring on terrains varying  
Figure 4: Violin diagram of 37 groups of debris flow sample data S1–S10 risk assessment index in Yunnan Province. from gentle to steep, emphasizing the importance of topographical undulation in the formation of debris flows. The data for evaluation indicators S3 and S7 are concentrated in the lower half of the dataset; S3 ranges from a minimum of 0.15 to a maximum of 102.8, with a median of 8.5 and an average of 18.72. This concentration trend suggests that the majority of watersheds affected by debris flows are relatively small. For S7, the minimum is \(-61\), the maximum is 7500, the median is 1300, and the average is 6053.49. The distribution characteristics of this indicator suggest that although most areas where debris flows occur have low vegetation coverage, the role of vegetation cover in debris flow occurrence is complex; it can potentially reduce the occurrence of debris flows through soil retention and water conservation, but in certain cases, such as after vegetation destruction, it may increase the risk of debris flows. The common conditions of small watershed areas and low vegetation coverage may be typical for debris flow occurrence. The data distribution for evaluation indicators S8, S9, and S10 is relatively uniform, indicating diversity in these factors within the debris flow risk assessment and not concentrated under specific conditions. Overall, the debris flow sample data from Yunnan Province exhibit significant diversity, which may imply the presence of substantial overlapping information within the samples, necessitating further data processing and analysis to extract key features. To verify the presence of redundant information among the indicators and the need for further processing, this study employs IBM SPSS Statistics 29 software to conduct a correlation analysis on the debris flow sample data.