## 5 Discussion
### Comparison of Different Negative-Sample Acquisition Strategies  
In previous studies, researchers tended to pay little attention to areas with little or no potential for debris flow. The acquisition of negative samples is therefore usually problematic. Random acquisition from unlabeled samples was the most common method used in previous studies [13]. However, both positive and negative samples were present in the unlabeled samples, so the random selection approach is usually unreliable. [PERSON] et al. [14] quantified the reliability of the negative samples obtained based on the difference between positive and negative samples, which is an important step in negative-sample acquisition. [PERSON] et al. [15] evaluated the correlation between geological hazards and conditioning factors; the reliable negative samples were obtained from areas with a poor correlation with the occurrence of geological hazards. It can be seen that establishing a set of criteria to identify negative samples from unlabeled samples in geographic or data spaces is the key problem to be solved.  
This paper proposed three negative-sample acquisition strategies based on SVM, spy technique, and IF methods. Each strategy has different assumptions of the datasets. The SVM-based strategy is mainly based on the assumption that there are far more negative samples than positive samples in the unlabeled samples; most of the randomly selected unlabeled samples are still reliable. A model is therefore able to present a probability, which is proportional to the actual probability for each sample, and reliable negative samples can be generated from samples with lower probabilities. The spy-based strategy is mainly based on the assumption that spy samples and potential debris flow samples from unlabeled samples will behave similarly in a model's prediction. By observing the probability threshold of spy samples, reliable negative samples can be identified. The IF-based strategy assumes that there are more negative samples than positive samples in the study area, and positive samples and negative flow samples should exhibit large differences. Then, most of the unlabeled samples can be considered as normal negative samples, and the potential debris flow samples can be considered as \"abnormal samples\". Based on the idea of anomaly detection, the IF algorithm can assign an anomaly score to each sample. Samples with lower anomaly scores can be regarded as reliable negative samples.  
To summarize, the negative-sample acquisition strategy based on the spy technique has no special requirements for either the datasets or the algorithms, which can explain the good performance of the strategy on multiple datasets. The IF-based negative-sample acquisition strategy assumes that debris flow samples are abnormal samples, which account for a relatively small portion of the overall datasets. For the grid datasets, grids with the same or similar characteristics as the debris flow samples are widely distributed throughout the study area, so the IF-based negative-sample acquisition strategy performed poorly in the grid datasets. On the other hand, the watershed unit datasets fit this assumption well, which shows that the IF-based negative-sample acquisition strategy performed well in the watershed unit datasets. For the SVM-based negative-sample acquisition strategy, it has some requirements for both the datasets and the algorithms, which is the reason for the unstable performance of this strategy in different datasets. On the other hand, the IF-based strategy proved to be the most efficient strategy, followed by the SVM-based strategy. The spy-based strategy had the worst efficiency. Overall, in practical applications, selecting reliable negative samples based on the distribution characteristics of the datasets is the key to effectively improve the reliability of negative samples.