# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
###### Abstract
Additionally, hybrid models combining CNN and LSTM have been proposed to address the spatiotemporal complexity of landslide data, with promising results in both spatial feature extraction and temporal dependency modeling [17; 18; 19; 20; 21]. Long Short-Term Memory (LSTM) networks have shown notable advantages in modeling and forecasting landslide displacement due to their capability to capture long-term temporal dependencies and nonlinear relationships in sequential data. Unlike traditional neural networks, LSTM can retain and selectively forget past information, making it highly suitable for analyzing complex hydrometeorological conditions leading to landslides. [PERSON] et al. (2021) applied LSTM to flood susceptibility prediction and found it outperformed traditional machine learning models in both sensitivity and specificity, thanks to its sequential data modeling capacity [22]. Similarly, [PERSON] et al. (2025) used LSTM to predict landslide displacement in the Italian Alps by incorporating rainfall, temperature, humidity, and snowmelt data. The results showed that LSTM models effectively captured the delayed and cumulative effects of snowmelt and precipitation, outperforming other configurations in both early-stage and long-term prediction scenarios [23]. Overall, LSTM's ability to model time-series data makes it a powerful tool for landslide forecasting, especially in environments where landslide movement is influenced by multiple interrelated factors such as rainfall, snow cover, and seasonal temperature variations. Convolutional Neural Networks (CNNs) have gained prominence in landslide susceptibility mapping due to their powerful capabilities in spatial feature extraction and image interpretation. CNNs can automatically learn and extract complex features from satellite imagery and digital elevation models (DEMs), making them highly effective in capturing nonlinear and spatially heterogeneous triggering factors such as topographic curvature, slope, aspect, weathering, and hydrological conditions. For example, in the Gorzineh-khil region of Iran, a 15-layer CNN model integrating both topographic and hydrological factorsachieved superior predictive performance--79% accuracy, 73% precision, 75% recall, and 77% F1-score--significantly outperforming conventional models such as SVM (70%), k-NN (65%), and decision trees (60%) [24]. Similarly, in a study conducted in Icheon, South Korea, CNNs combined with metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO) and Imperialist Competitive Algorithm (ICA) demonstrated enhanced performance in susceptibility mapping by improving both spatial generalization and predictive precision [25]. These findings underline CNN's robustness in processing high-dimensional data and modeling complex spatial patterns, making it a powerful deep learning approach for landslide prediction, particularly in regions with diverse geomorphological and hydromechanical conditions. Spatial Attention Mechanism (SAM) have shown notable benefits in landslide detection and prediction by enhancing feature focus, reducing background noise, and improving boundary precision. [PERSON] et al. (2022) demonstrated that attention-based models like STANet and SNUNet outperform traditional CNNs in segmentation accuracy [26]. [PERSON] et al. (2024) combined SAM with LSTM, improving landslide susceptibility mapping in complex terrains [27]. [PERSON] et al. (2022) proposed OC-ACNN, integrating statistical knowledge and attention to boost spatial prediction accuracy [28]. [PERSON] et al. (2022) introduced a multi-head self-attention LSTM model that captured abrupt displacement patterns more effectively [29]. Overall, SAM enhances landslide modeling by capturing key spatial-temporal features and improving generalization in varied and complex environments.