## 4 Conclusions  
In this study, an attempt was made to use the total area of catchment, average slope of the catchment, the length of the stream, the average slope of the stream bed, the shape coefficient, the type of stratum, the distance from the region's central fault, and the trigger  
Figure 19: Receiver operating characteristic curves for logit regression analysis.  
Figure 20: Receiver operating characteristic curves for the instability index method.  
factors. The sensitivity analysis of the geotechnical potential zone was conducted by using eight variables, together with the instability index, Rogers regression, and neural network. The comparison of the results with those of previous studies revealed that the importance of the variables overlapped with each other, but nevertheless, the importance of the factors affecting the geotechnical potential zones would be different in different regions due to the different characteristics of the environments.  
This paper's evaluations of the instability index method, logit regression analysis, and neural network analysis as potential means of precisely predicting debris-flow disasters revealed that each has its benefits and drawbacks. The following conclusions can be drawn from the results. First, the three methods' general accuracy rates ranged from 70.9% to 93.3%. When it came to the prediction of debris-flow disasters, however, the spread was much wider: 83.7% for neural networks, 48.1% for logit regression analysis, and 32.7% for instability index analysis. In predicting the absence of any debris-flow disaster, logit regression analysis performed best, at 98.4%; neural network analysis almost as well, at 97.4%; and the instability index method the least well, at 87.1%. According to neural network analysis, the three most important of the authors' eight selected debris-flow triggers (in descending order of importance) were (1) total catchment area, (2) maximum hourly rainfall, and (3) average slope of the catchment, while the least important was the distance from a fault. This study's AUC of the ROC curve results suggest that all three of the tested models can be used for this purpose but that the neural network has the best discrimination ability (i.e., excellent) and the instability index method the worst (i.e., fair).  
Future researchers are encouraged to verify this paper's results using data from weather events in other regions. Also, its list of potential factors that trigger debris flows should not be regarded as exhaustive. The future identification of other such factors would only tend to improve the performance of debris-flow prediction models. Additionally, the present work's results suggest that when using the instability index method, the initial factor classification could usefully divide factors into more intervals, thereby improving the precision of its judgment of areas' landslide potential. The accuracy of logit regression, meanwhile, would be improved if more debris flows were included to balance the number of cases with and without such flows. Lastly, this study did not include preservation objects such as residences and public infrastructure. Including them would enrich this line of research as a basis for future novel approaches to slope-disaster risk analysis.  
Conceptualization, J.-Y.L.; data curation, L.-K.Y.; writing--original draft, L.-K.Y.; writing--review and editing, J.-C.C.; supervision, J.-Y.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. We would like to thank the anonymous reviewers for their helpful suggestions and feedback. The authors declare no conflicts of interest.