## 1 Introduction  
In this time of climate change, Taiwan's rainy seasons are becoming shorter but more intense, increasing the likelihood of extreme rainfall events and rain-related disasters. In mountainous areas, episodic events such as landslides, debris flows, and avalanches are the main drivers of sediment migration [1]. A debris flow is a saturated mixture of granular dirt, organic matter, and other debris that moves in a steep, specified channel at an incredibly fast rate [2]. Due to their ability to quickly displace other material, debris flows are among the most highly destructive hydrogeomorphic processes and present serious risks to infrastructure and people [3]. One of their main causes is intense precipitation, and mudslides caused ultimately by rainfall are typically triggered by debris flows [4, 5]. According to various studies, global warming may lead to an increase in the number of extreme precipitation occurrences worldwide [6, 7]. Hazard studies focused on landslide-induced debris flows tend to include thorough the inventories of landslides as a means of understanding the primary factors that govern and trigger their occurrence [3, 8]. These governing factors, in turn, are tied to influencing conditions that vary according to each area's features [3, 9]. In the wake of a pioneering study by [PERSON] (1958), there have been numerous attempts to pinpoint the crucial morphological factors that influence the inception of landslides in particular catchment areas [10, 11, 12, 9]. Landslides caused by the 1999 Jiji earthquake in Taiwan considerably increased both the numbers and the sizes of the country's subsequent rainfall-driven landslides [13; 14], and a similar process was also observed in areas severely damaged by the 2008 Wenchuan earthquake [15; 16; 17]. Over the past decade, scientists have investigated a number of approaches to evaluate the risks posed by debris flows, a process critical to disaster mitigation and prevention [13; 18; 19; 20; 15]. Following earthquakes, modeling based on geographic information system (GIS) is frequently utilized as a means of hazard assessment [21; 22], as are statistical analysis [23; 24; 25], dynamic methods such as Dynamic Analysis and the three-dimensional model DAN [26; 27], the interpretation of aerial photographs or satellite images [28; 29], and debris-flow monitoring [30; 31]. One of the most-used techniques in the multivariate analysis of areas' landslide susceptibility is logistic regression, also known as logit regression or LR [32; 33]. Recent studies of debris flows have also used LR [34; 35] and found its performance in determining the key variables that influence the occurrence of such flows to be satisfactory. More specifically, as compared to previous multivariate techniques of landslide susceptibility investigation, LR has lower error magnitudes [35]; this could potentially also be the case if the topic of this study were debris flows. Although slope catastrophe assessments are still carried out traditionally, i.e., via in-person inspection, many hilly regions with exceptionally steep terrain remain inaccessible. The present work is intended to help address that problem by interpreting debris-flow disaster events using satellite images taken before and after they occurred, thus greatly expanding the number of available examples. Remote sensing images can be used as a basis for the thorough detection and analysis of large-scale areas and to improve prediction capabilities using environmental characteristics, which can usefully be subdivided into terrestrial, material, and trigger factors. In order to prevent the occurrence of landslide disasters and the planning and utilization of national land, it is obviously an important task to investigate the potential areas of geotechnical flows and to establish the sensitivity prediction of the potential areas of geotechnical flows.