# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 1 Introduction  
Landslides represent a frequent natural hazard globally, endangering lives and infrastructure in mountainous and reservoir regions in particular [1]. The Three Gorges Reservoir area exemplifies these challenges, where complex geological conditions and harsh environments trigger frequent landslides along the Yangtze River. In recent years, periodic water level fluctuations, extreme rainfall, and accelerated urbanization have further exacerbated the risk of landslides [2]. Over the past decade, nearly 5000 landslides have been recorded in this region, including many highly destructive cases. Three key factors drive landslide proliferation: fragile geological structures, steep slopes, and intensiveconstruction activities. These conditions promote slow-moving landslides that accumulate structural damage over time. Surface deformations during slope movements generate ground fissures and localized collapses. Such deformations escalate maintenance costs, while posing critical safety risks for residents. Effective landslide susceptibility mapping addresses these challenges by enabling proactive risk assessments. Advanced predictive modeling enhances early warning capabilities and supports targeted mitigation strategies, ultimately strengthening community resilience and guiding sustainable land-use planning. Landslide susceptibility provides a critical framework for assessing regional landslide risks by synthesizing geological, topographic, and meteorological factors [3; 4; 5; 6]. This probabilistic evaluation depends on a systematic analysis of the predisposing factors to predict landslide likelihood and spatial patterns [7; 8; 9; 10; 11]. Three primary modeling approaches dominate current methodologies: statistical, physical, and machine learning models. Statistical models predict landslide occurrence probabilities based on historical data through statistical analysis [12]. These models have been effectively demonstrated in regard to national and medium-scale assessments, using public datasets [13; 14; 15]. Theoretical models, based on physical laws, simulate slope failure mechanisms through the use of mechanistic equations, achieving high precision in controlled scenarios, but requiring exhaustive geotechnical inputs that often prove impractical for regional applications [16; 17; 18; 19]. Recent advances in data-driven machine learning techniques have revolutionized landslide prediction through the use of automated pattern recognition in high-dimensional datasets. For instance, [PERSON] et al. (2021) [20] and [PERSON] et al. (2022) [21] developed deep convolutional neural networks for mapping landslide susceptibility [22]. They proposed a meta-learning approach that utilized Bayesian optimization for hyperparameter tuning, employing random forests as the predictive model. Additionally, [PERSON] et al. (2023) [23] introduced an ML-based explainable algorithm, SHapley Additive exPlanations, for landslide susceptibility modeling. Ensemble learning architectures enhance prediction reliability by strategically combining multiple models, with homogeneous and heterogeneous approaches offering distinct advantages for landslide susceptibility mapping [24; 25]. Homogeneous ensembles (e.g., random forest, XGBoost, LightGBM, and CatBoost) employ collections of the same base algorithm, typically decision trees, to reduce variance through the use of parallel training with bootstrap aggregation (bagging) or gradient boosting [26; 27]. While these methods excel at handling high-dimensional data and mitigating overfitting, they may exhibit limited adaptability when geological processes require fundamentally different algorithmic approaches. In contrast, heterogeneous ensembles (e.g., bagging, stacking) integrate diverse machine learning models to leverage their complementary strengths [28; 29; 30]. Stacking, for instance, uses a meta-learner to optimally combine base model predictions, often improving performance in regard to complex, nonlinear landscapes.