Multiple data-driven approach for predicting landslide deformation

Abstract

Currently, mathematical models such as the regression model, grey prediction model, and neural networks are commonly used to predict landslide displacement. In this study, we develop multi-data-driven models for prediction of landslide displacement. Multi-kernel learning and weighted learning methods are integrated with the grey prediction theory and three models are constructed to predict landslide displacement: the kernel-based grey model, multi-kernel grey model, and weighted multi-kernel grey model. All three models are simple to calculate and easy to program. The models were applied to a case study of the Baishuihe landslide in the Three Gorges reservoir, China. The influence of rainfall, reservoir water level, landslide rate, and historical displacement on the current displacement of the landslide were investigated, and multi-data-driven prediction models with input parameters (rainfall, reservoir water level, landslide rate, and historical displacement) and an output parameter (current displacement) were developed. The results show that the weighted multi-kernel grey model has better prediction accuracy than the kernel-based grey model and the multi-kernel grey model. These findings can be widely applied in practical landslide prediction.