Slope Displacement Prediction and Risk Mitigation based on Artificial Intelligence Algorithm

Effective prediction of slope displacement is essential for mitigating landslide risks, ensuring infrastructure stability, and enhancing early warning systems. Traditional monitoring techniques often struggle to capture the complex interactions between hydrological, and environmental factors, necessitating advanced computational approaches for improved predictive accuracy. This research introduces a novel deep learning framework utilizing a one-dimensional Convolutional Neural Network (1D CNN) to enhance slope displacement prediction accuracy. The proposed model is trained on the Slumgullion Landslide Monitoring Data, which provides high-resolution, long-term displacement measurements. By leveraging the feature extraction capabilities of 1D CNNs, the model effectively identifies hidden patterns in time-series displacement data, enabling adaptive and precise forecasting. Experimental findings confirm the model’s outstanding performance, achieving a Mean Absolute Error (MAE) of 0.018 mm, Root Mean Square Error (RMSE) of 0.027 mm, and an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.97, highlighting its robustness and predictive accuracy. Comparative analysis against traditional learning models confirms the efficiency of the proposed approach in capturing spatial-temporal dependencies. These results underscore the potential of AI-driven methodologies in developing landslide monitoring and informing data-driven decision-making for geohazard management.