A data-driven intelligent model for landslide displacement prediction

Landslides with step-like deformation features are widely distributed in the Three Gorges Reservoir area (TGR) of China, posing a severe hazard to the inhabitants of this region. This paper proposes a multi-input and multi-output intelligent integrated displacement prediction model for landslides with step-like displacement patterns. In this new model, three interconnected and information-transmitted functional sub-models are integrated. Unsupervised learning is used to identify different landslide deformation states automatically, and the imbalance classification and explainable artificial intelligence techniques are introduced for qualitative prediction and information filtering. Probability theory and deep machine learning are adopted to provide deterministically predicted values and quantify their uncertainty. The case study of the Baijiabao landslide in the TGR region proves that the proposed model performs satisfactorily in both point and interval predictions. The intelligent integrated model can also provide the forecast of landslide deformation states, visual input information filtering and back analysis of influencing factors, which are valuable to landslide early warning and risk management.