###### Abstract  
The lack of reliable negative samples is an important factor limiting the quality of machine learning-based debris flow susceptibility mapping (DFSM). The purpose of this paper is to propose multiple negative-sample acquisition strategies for DFSM considering different sample representation forms. The sample representation forms mainly include a single grid, multi-grid, and watershed unit, and the negative-sample acquisition strategies are based on support vector machine (SVM), spy technique, and isolation forest (IF) methods, respectively. These three strategies can assign a value to all the samples based on different assumptions, and reliable, negative samples can be generated from samples with values below a predefined threshold. Combining different sample representation forms with negative sample acquisition strategies, nine datasets were then involved in random forest (RF) modeling. The receiver operating characteristic (ROC) curves and related statistical results were used to evaluate the models. The results show that the strategy based on the spy technique is suitable for multiple datasets, while the IF-based strategy is well-adapted to the watershed unit datasets. This study can provide more options for improving the quality of datasets in DFSM, which can further improve the performance of machine learning models. debris flow susceptibility mapping; machine learning; negative-sample acquisition; sample representation form +
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samples can usually be obtained from multiple sources, and samples obtained through different sources can be mutually verified to ensure their reliability. Conversely, the process of obtaining negative debris flow samples is usually not guaranteed to be reliable. In previous studies, negative samples were often obtained randomly from unlabeled samples [13]. This approach would introduce potential data noise into the datasets. [PERSON] et al. [14] quantified the reliability of the negative samples obtained based on the difference between positive and negative samples. Negative samples with reliability values higher than a specific threshold were used for modeling. The determination of optimal thresholds needs to be explored further. [PERSON] et al. [15] quantitatively evaluated the correlation between geological hazards and the conditioning factors; the reliable negative samples were obtained from areas with a poor correlation with an occurrence of geological hazards. This method has some high requirements for both the algorithm design and the number of positive samples. [PERSON] et al. [16] proposed that negative samples could be obtained by replacing some features of positive samples. However, negative samples obtained in this way may not exist in the real world. [PERSON] et al. [9] treated some of the unlabeled samples as negative samples in combination with positive samples to build a model to predict all the samples. Samples with the lowest predicted value can be regarded as reliable negative samples. This paper proposed three negative-sample acquisition strategies based on support vector machine (SVM), spy technique, and isolation forest (IF) models, which can provide more appropriate options for reliable negative-sample acquisition. The key to obtaining negative samples is to establish an evaluation metric based on positive versus unlabeled samples. The sample representation forms should be an issue that deserves more attention. For debris flows, grids and watershed units are currently the most commonly used sample representation forms. Grids are generally suitable for storing and processing information at various geographical scales. But, they lack the ability to express the overall characteristics of a particular object [11]. Watershed units, as the basic unit of occurrence of a debris flow event, can have some overall characteristics that are closely related to debris flow formation, but its acquisition process is usually more complicated [17]. In the present study, both grids and watershed units were considered for comparison.