Rainfall-induced landslides are a major hazard in the Three Gorges Reservoir area (TGRA) of China, encompassing 19 districts and counties with extensive coverage and significant spatial variation in terrain. This study introduces the Gradient Boosting Decision Tree (GBDT) model, implemented on the Google Earth Engine (GEE) cloud platform, to dynamically assess landslide risks within the TGRA. Utilizing the GBDT model for landslide susceptibility analysis, the results show high accuracy with a prediction precision of 86.2% and a recall rate of 95.7%. Furthermore, leveraging GEE’s powerful computational capabilities and real-time updated rainfall data, we dynamically mapped landslide hazards across the TGRA. The integration of the GBDT with GEE enabled near-real-time processing of remote sensing and meteorological radar data from the significant “8–31” 2014 rainstorm event, achieving dynamic and accurate hazard assessments. This study provides a scalable solution applicable globally to similar regions, making a significant contribution to the field of geohazard analysis by improving real-time landslide hazard assessment and mitigation strategies.

Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R² value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model’s 72.3% and the SVM model’s 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research.