# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
###### Abstract  
Accurate prediction of landslide susceptibility is a key component of disaster risk reduction and early warning systems. Traditional landslide susceptibility prediction methods often face challenges in capturing complex nonlinear and spatio-temporal relationships inherent in geospatial data. In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. The model is trained on a comprehensive dataset comprising 19,898 samples, constructed from landslide records and 16 influencing factors in Kumamoto Prefecture, Japan. The input dataset is processed in tabular format using Microsoft Excel and includes variables such as topography, meteorology, soil characteristics, and human activity. The proposed model leverages Convolutional Neural Networks (CNN) to extract spatial features, Long Short-Term Memory networks (LSTM) to model temporal dependencies, and a Spatial Attention Mechanism (SAM) to enhance feature weighting dynamically. Experimental results demonstrate that the CNN-LSTM-SAM-Attention model significantly outperforms traditional machine learning approaches in terms of accuracy, precision, recall, F1 score, ROC-AUC, and PR-AUC. This substantial improvement is attributed to the model's enhanced capability in capturing complex spatio-temporal patterns and dynamically weighting critical spatial features through the integrated Spatial Attention Mechanism (SAM). This study highlights the potential of deep learning-based approaches for improving the reliability of spatial landslide susceptibility prediction in complex terrain and dynamic climatic conditions. l 202524 June  
are difficult to gather. Additionally, physical mechanics techniques have more computing complexity and more stringent usage requirements [2]. In the subject of landslide prediction, machine learning model prediction techniques and conventional statistical prediction techniques are currently gaining traction [3; 4; 5]. Weight of evidence (WOE), logistic regression, generalized linear models (GLM), generalized additive models (GAM), and multivariate statistical analysis are examples of traditional statistical prediction models that rely on historical data and preset thresholds [6; 7]. While these models are simple to use, they are unable to capture the complex dependencies and nonlinear relationships present in landslide data [8]. The shortcomings of traditional statistical prediction models in capturing complex dependencies and nonlinear relationships have been addressed by machine learning techniques like decision trees [9], support vector machines (SVMs) [10], and random forests [11; 12]. However, these techniques still have difficulty handling the high dimensionality and temporal dynamics of the data, and while their predictive ability is superior to that of traditional statistical models, it has demonstrated very little difference in predictive performance in a number of real-world projects [13; 14; 15]. Deep learning techniques, known for their ability to automatically extract significant features from large and complex datasets, have become increasingly prevalent in landslide prediction models in recent years. Various studies have demonstrated the effectiveness of deep learning in enhancing prediction accuracy by handling both spatial and temporal data. For instance, [PERSON] et al. (2022) compared the performance of convolutional neural networks (CNNs) and conventional machine learning methods in landslide susceptibility mapping, showing that CNN models outperform traditional approaches due to their ability to capture intricate spatial patterns [16; 17; 18]. Similarly, [PERSON] et al. (2022) explored the potential of recurrent neural networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, in modeling temporal dependencies and forecasting landslide occurrences triggered by rainfall and earthquakes.