## 1 Introduction  
Debris flows are a common geological hazard characterized by their sudden onset, destructive power, and wide-ranging impacts, often posing severe threats to human lives, property, and ecological systems [1]. Accurately predicting the spatial distribution and susceptibility of debris flows is not only a critical focus of disaster prevention and mitigation, but also an urgent scientific challenge in susceptibility assessment [2]. Traditionally, debris flow susceptibility evaluations have relied on subjective experience and statistical methods, often complemented by remote sensing and geographic information system (GIS) analyses. For single-channel debris flows, field investigations and numerical simulations are typically employed to delineate deposition areas [3]. For regional-scale assessments, susceptibility mapping is generally conducted using GIS-based statistical analyses combined with regional background data and remote sensing imagery. Despite their foundational contributions, traditional methods have demonstrated limitations in complex geological environments and large-scale assessments. Field investigations and expert-driven approaches are constrained by human resources and time efficiency, making the comprehensive coverage of large areas challenging. Moreover, qualitative methods, such as the Analytic Hierarchy Process (AHP) [4; 5], rely heavily on expert judgment, resulting in subjective and arbitrary evaluation outcomes [6; 7]. Statistical methods, including Weight of Evidence [8; 9] and Frequency Ratio [10; 11], struggle to capture nonlinear interactions among terrain, geology, hydrology, and other environmental factors [12]. Additionally, numerical simulations demand high-quality data and computational resources, encountering bottlenecks in efficiency and precision when processing large-scale remote sensing datasets. In recent years, advancements in remote sensing and artificial intelligence have led to the growing adoption of machine learning algorithms in debris flow susceptibility assessments. In this context, algorithms such as logistic regression [13; 14], Bayesian models [15; 16], convolutional neural networks [17; 18], Support Vector Machines [19], Random Forests [20; 21; 22], and Extreme Gradient Boosting (XGBoost) [23; 24; 25] have gained popularity. Compared to traditional models, machine learning methods offer advantages such as the ability to handle large datasets, strong generalization capabilities, efficient modeling processes, and higher predictive accuracy [26]. These methods can effectively capture the nonlinear relationships between debris flow susceptibility and environmental factors [27]. Consequently, machine learning algorithms often outperform traditional statistical models and are widely used in debris flow susceptibility evaluations [28]. The western Sichuan region of China is highly susceptible to debris flow disasters due to the combined influences of monsoonal circulation, high-energy terrain, and heterogeneous underlying surface conditions. In recent years, improvements in transportation infrastructure and the rapid growth of tourism have significantly enhanced regional accessibility while simultaneously exerting considerable pressure on the ecological environment [29; 30; 31]. Large-scale construction activities, transportation infrastructure development, and tourism have led to the destruction of vegetation and soil degradation. Unmanaged mining pits, quarries, and waste disposal sites have provided abundant sources of debris material, exacerbating the occurrence of debris flows [32; 33]. Although numerous studies have been conducted on debris flow susceptibility in this region, most have focused on specific areas [34; 35; 36], limiting their applicability to broader contexts. Some studies have evaluated susceptibility at larger scales, such as Sichuan Province or the upper Minjiang River basin [37; 38; 39; 40], but their coarse spatial resolution often fails to provide detailed guidance for disaster prevention and mitigation efforts.