The prediction of active landslide displacement is a critical component of an early warning system and helps prevent property damage and loss of human lives. For the colluvial landslides in the Three Gorges Reservoir, the monitored displacement, precipitation, and reservoir level indicated that the characteristics of the deformations were closely related to the seasonal fluctuation of rainfall and reservoir level and that the displacement curve versus time showed a stepwise pattern. Besides the geological conditions, landslide displacement also depended on the variation in the influencing factors. Two typical colluvial landslides, the Baishuihe landslide and the Bazimen landslide, were selected for case studies. To analyze the different response components of the total displacement, the accumulated displacement was divided into a trend and a periodic component using a time series model. For the prediction of the periodic displacement, a back-propagation neural network model was adopted with selected factors including (1) the accumulated precipitation during the last 1-month period, (2) the accumulated precipitation over a 2-month period, (3) change of reservoir level during the last 1month, (4) the average elevation of the reservoir level in the current month, and (5) the accumulated displacement increment during 1year. The prediction of the displacement showed a periodic response in the displacement as a function of the variation of the influencing factors. The prediction model provided a good representation of the measured slide displacement behavior at the Baishuihe and the Bazimen sites, which can be adopted for displacement prediction and early warning of colluvial landslides in the Three Gorges Reservoir.
A good prediction of landslide displacement is an essential component for implementing an early warning system. In the Three Gorges Reservoir Area (TGRA), many landslides deform distinctly and in steps from April to September each year under the influence of seasonal rainfall and periodic fluctuation in reservoir water level. The sliding becomes more uniform again from October to April. This landslide deformation pattern leads to accumulated displacement versus time showing a step-wise curve. Most of the existing predictive models express static relationships only. However, the evolution of a landslide is a complex nonlinear dynamic process. This paper proposes a dynamic model to predict landslide displacement, based on time series analysis and long short-term memory neural network (LSTM). The accumulated displacement was decomposed into a trend term and a periodic term in the time series analysis. A cubic polynomial function was selected to predict the trend displacement. By analyzing the relationships between landslide deformation, rainfall and reservoir water level, a LSTM model was used to predict the periodic displacement. The LSTM approach was found to more properly model the dynamic characteristics of landslides than static models, and make full use of the historical information. The performance of the model was validated with the observations of two step-wise landslides in the TGRA, the Baishuihe landslide and Bazimen landslide. The application of the model to those two landslides demonstrates that the LSTM model provides a good representation of the measured displacements and gives a more reliable prediction of landslide displacement than the static support vector machine (SVM) model. It is concluded that the proposed model can be used to effectively predict the displacement of step-wise landslides in the TGRA.
Within the engineering profession and natural sciences, vulnerability is widely accepted to be defined as the degree of loss (or damage) to a given element or set of elements within the area affected by a threat. The value of vulnerability is expressed nondimensionally between 0 and 1. It is a fundamental component in the evaluation of landslide risk, and its accurate estimation is essential in making a reasonable prediction of the landslide consequences. Obviously, vulnerability to landslides depends not only on the characteristics of the element(s) at risk but also on the landslide intensity. This paper summarizes previous research on vulnerability to landslides and proposes a new quantitative model for vulnerability of structures and persons based on landslide intensity and resistance of exposed elements. In addition, an approximate function is suggested for estimating the vulnerability of persons in structures. Different methods for estimating the vulnerability of various elements to slow or rapid landslides are discussed. Finally, the application of the new model is illustrated through an example.
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