# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
###### Abstract
However, a critical limitation persists in existing studies: they predominantly apply CNN and LSTM in isolation, thereby failing to adequately capture and model the essential dynamic coupling mechanism of spatiotemporal features that drive landslide initiation [30; 31; 32]. Landslide triggering is frequently caused by complex multi-factor spatiotemporal interactions, including geological activity, rainwater penetration, and terrain stability, which demand integrated spatiotemporal analysis. Landslide triggering is frequently caused by multi-factor spatiotemporal interactions, including geological activity, rainwater penetration, and terrain stability. To directly address this limitation of isolated spatiotemporal modeling, this study proposes a CNN-LSTM-SAM-Attention landslide prediction model. This integrated architecture explicitly captures the dynamic coupling of spatiotemporal features by synergistically combining spatial feature extraction (CNN), temporal dependency modeling (LSTM), and dynamic feature weighting (SAM-Attention mechanism) within a cohesive spatio-temporal analytic framework [30; 31; 32]. In order to identify long-term dependencies in time-series data, the model first uses a CNN to extract important characteristics from the original data. Lastly, the self-attention mechanism highlights the important spatiotemporal aspects by weighting the LSTM's output [33; 34]. This study's primary contributions: (1) The novel integration of CNN, LSTM, and SAM self-attention mechanism within a single, end-to-end architecture. This specifically overcomes the key limitation of existing methods identified above--their inability to model the dynamic coupling of spatiotemporal features--by enabling simultaneous and synergistic extraction of spatial patterns, modeling of temporal dependencies, and dynamic weighting of salient features. (2) Compile information about the 2020 Kumamoto Prefecture landslide, build a database, and use the model suggested in this research to train and forecast. (3) Train and predict using conventional machine learning models on the same datasets and then compare the model evaluation index with the model suggested in this paper. It is demonstrated that the CNN-LSTM-SAM-Attention model can successfully capture the spatio-temporal characteristics of the data, deal with the complex and non-linear relationships in geospatial and temporal data, and then improve the accuracy of landslide prediction by comparing evaluation indices like accuracy, precision, recall, F1 score, ROC-AUC, and PR-AUC [35,36]. This is how the remainder of the paper is structured. Section 2 presents the CNN-LSTM-SAM-Attention model's process framework and each module's underlying principles. Section 3 creates a database of landslide samples and describes the engineering background of the landslides in Kumamoto Prefecture. The model's training procedure and actual outcomes are detailed in Section 4, which also analyzes and assesses the model in light of the findings. In Section 5, a traditional machine learning model is used to predict landslides on the same landslide sample database. The performance differences between the CNN-LSTM-SAM-Attention model suggested in this paper and the traditional machine learning model are compared. The conclusion and prospects for further study are presented in Section 6.