## 1 Introduction  
The Three Gorges Project, recognized as the largest hydraulic and hydropower project globally, is located in a mountainous region along the middle and upper reaches of the Yangtze River, extending from Yichang to Chongqing in China. The reservoir, characterized by its long and narrow shape, spans over 600 km behind the Three Gorges Dam and is considered one of the most geohazard-prone areas in China. This susceptibility is attributed to the complex geological environment and the reservoir's impoundment since 2003 [1,2]. Over 4000 geological hazards and potential danger points have been identified within the Three Gorges Reservoir Area (TGRA), with nearly 90% classified as landslides and rockfalls [3]. The occurrence and resurgence of potentially catastrophic landslides may induce slope deformation and collapse, trigger debris flows, and even generate impulse waves (or tsunamis), thereby posing significant threats to the safety of residents, property, critical water infrastructure, and the ecological environment of the TGRA [4, 5, 6]. On 13 July 2003, the Qianjiangping landslide occurred in Zigui County after 43 days of reservoir impoundment. The sliding mass entered the water at a high velocity, generating a wave exceeding 30 m on the opposite bank, which overturned 22 vessels and resulted in 14 fatalities. This incident impacted nearly 1000 individuals, with direct economic losses surpassing CNY 50 million [6]. After more than a decade of prevention and mitigation efforts, most small- and medium-scale landslides in the Three Gorges Reservoir area have been effectively managed. However, large-scale landslides, such as the Huangtopo, Huangflashi, Outang, and Ximpu landslides, remain inadequately addressed due to technological limitations [7, 8, 9, 10], including the complexity of deep-seated and fragmented geology, the limitations of conventional stabilization methods, high costs, and logistical challenges. Real-time monitoring, coupled with timely forecasting and early warning systems, serves as a crucial safeguard against the catastrophic risks posed by these landslides. Extensive monitoring data indicate that the deformation process of large reservoir landslides in the TGRA typically progresses through a phase of creep followed by a sudden \"step-like\" change [11]. Real-time monitoring not only captures precursor characteristics preceding instability but also provides a reliable foundation for disaster early warning. The generation of extensive multi-field monitoring data during landslide observation has become a significant basis for analyzing movement patterns, assessing stability, and informing disaster prevention measures, allowing researchers to explore the potential relationships among various monitoring objects [12, 13, 14]. Traditional data analysis methods often struggle to meet the demands of large, multi-source monitoring datasets due to their reliance on predefined models, their limited flexibility in handling heterogeneous data, and the inability to process large datasets effectively. In contrast, data mining technologies can rapidly extract valuable information from extensive landslide monitoring data, leveraging their algorithmic capabilities. These technologies have been widely applied in various aspects of landslide research, such as landslide recognition [15], prediction [16,17], and susceptibility analysis [18,19]. Existing large reservoir-induced landslides in the TGRA, defined as landslides triggered by changes in reservoir conditions and with a volume greater than 1 million m\({}^{3}\) according to the national code GB/T 32864-2016 [20], are prone to reactivation or accelerated deformation due to a combination of internal and external triggering factors, including tectonic activity, intense precipitation, water level fluctuations, bank erosion, human activities, and seismic loading [21, 22, 23, 24].