Landslide Prediction Model Based Upon Intelligent Processing of Multi-Point Monitoring Information: A Review

The construction of landslide displacement prediction model is crucial and effective in landslide prevention and mitigation. Intelligent processing methods are deeply required owing to the boost of monitoring datasets. Machine learning (ML) and deep learning (DL) methods are widely applied in landslide prediction. In this paper, aiming at the hydrodynamic pressure-driven landslides with step-like features in China Three Gorges Reservoir (CTGR) area, the improved ensemble learning models integrating the advantages of multifarious algorithms as well as considering the time series are proposed for prediction. Due to the limitations of monitoring sites and data missing, the multi-feature fusing transfer learning (MFTL) method is proposed for landslide prediction. It transfers the knowledge learned form a landslide with enough data to the other landslides with insufficient data. The successful applications in landslide prediction based on intelligent processing of monitoring information provide the efficient ways for further studies.