## 1 Introduction  
Landslides are natural hazards in alpine regions around the world [1, 2, 3]. Climate change has increased the frequency and amplitude of landslides [4, 5, 6], endangering mountain people's property and lives and impeding social development [7, 8, 9]. The task of landslide susceptibility mapping (LSM) is typically assigned to regional landslide risk management and land use planning [10, 11]. Accurate evaluation and prediction of landslide susceptibility under dynamic conditions, such as climate change, can support areas in better planning and managing their natural resources and informing efforts to reduce and control landslide risk [12, 13]. Landslides are the result of a combination of dynamic and static variables [14]. Among them, static factors include soil composition, geology, topography, geomorphology, etc., whereas dynamic factors include climatic variables (temperature, heavy rainfall), land use/cover, and earthquakes [15]. Most previous landslide susceptibility results were based on static factors, which were assessed using both qualitative and quantitative methods. Qualitative methods mainly consist of heuristic models such as expert empirical methods [16, 17]. However, qualitative methods rely on subjective knowledge and the experience of experts, which can result in uncertainty in the results [18]. The quantitative approach is the calculation of landslide probability by physical models [19], which require a detailed, highly accurate catalog of landslides [20]. Statistical models cannot deal with complex nonlinear problems; hence, they cannot reveal the relationship between landslides and environmental conditions [21]. Machine learning methods, e.g., the logistic regression (LR) model, have the advantage of capturing complex relationships in data in landslide spatiotemporal susceptibility assessments, which is becoming more popular in landslide susceptibility assessments [22; 23]. Nevertheless, landslide susceptibility assessment methods still vary in their performance [24], suggesting that selecting the most appropriate method is crucial for accurately assessing and predicting regional landslide susceptibility. Recently, global warming has exacerbated landslide activities in alpine areas [25; 26]. The previous regional landslide susceptibility based on static factor assessments can no longer meet the requirements for regional disaster prevention and mitigation, and there is an urgent need for LSM that takes dynamic factors into consideration. A significant number of researchers have conducted extensive studies on picking one or more dynamic elements and integrating them with climate model prediction and In-SAR technologies to detect landslides [15; 27]. The superimposed In-SAR method is a fast and effective way to identify probable landslides in large mountainous areas [9; 28; 29]. This method has modest technical requirements and computing effort, making it suitable for monitoring landslide susceptibility in remote and challenging terrain. However, the In-SAR method may overlook some active slopes with tiny spatial scales, low displacement levels, and air effects [30]. Furthermore, the superimposed In-SAR technique is insufficient for predicting future landslide susceptibility. According to climate change projections, the severity and frequency of landslides would fluctuate due to changed precipitation and temperature regimes [31]. Additionally, dynamic landslide susceptibility mapping usually employs dynamic variables (e.g., cumulative rainfall and snowmelt) and static parameters (e.g., lithology and topographic features) as model inputs, and thus the use of a revised machine learning model, which is capable of evaluating the spatiotemporal probability of landslides, then realizes the assessment and prediction of dynamic landslide susceptibility [32; 33; 34; 35]. The model successfully overcomes the limitations of traditional static machine learning applications, and the results have the potential to be applied to early warning systems for disasters [34; 35; 36].