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Analyzing and quantifying the deformation process of landslides is of paramount importance in facilitating landslide early warning. As such, this study is committed to proposing a universal phenomenological model for deformation stages division and early warning of landslides based on the kinematic features. First, five landslide deformation patterns were classified based on the creep theory, and suggestions for stage division of each deformation pattern are presented. Then, the statistical characteristics of landslide velocity were analyzed, and a probability-based deformation stage division method was proposed. Finally, the Comprehensive Standardized Deformation Index (CSDI) model, which includes the calculation of the CSDI M - M (Min-Max normalization) and CSDI M (Mean normalization) was proposed and verified in 24 landslides worldwide. The results show that, except for the oscillating pattern, the CSDI M - M is feasible in the stages division of all deformation patterns with a strong correspondence with the actual state of the landslides. The CSDI M is a reliable landslide warning criterion and threshold determination method, as it is effective in the early warning of imminent landslides with a low false alarm rate. The CSDI model provides new insight into the division of landslide deformation stages and landslide risk assessment. [ABSTRACT FROM AUTHOR]

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]