A hybrid interval displacement forecasting model for reservoir colluvial landslides with step-like deformation characteristics considering dynamic switching of deformation states

Constructing an accurate and dependable displacement forecasting model is a prerequisite for realizing effective early warning systems of landslide disasters. To overcome the drawbacks of previous displacement prediction models for landslides with step-like deformation characteristics, such as the low prediction accuracy of the mutational displacements and the unclear reliability of the prediction results, we propose a novel hybrid interval forecasting model. This model consists of four parts. First, clustering by fast search and find of density peaks is implemented to distinguish the deformation states of the landslide. Second, the ensemble classifier based on the random forest algorithm is established to identify the deformation states. Third, based on the wild bootstrap, kernel extreme learning machine, and back propagation neural network approaches, the ensemble regressors under different deformation states are built. Finally, by combining the ensemble classifier and ensemble regressors, an interval prediction framework is constructed to realize the dynamic interval prediction of landslide displacement. Taking the Baishuihe landslide as an example, the datasets of three monitoring sites from June 2006 to December 2016 are used to verify the accuracy and reliability of the proposed model. The results show that the proposed model can effectively improve the prediction accuracy of mutational displacements, with the root mean square errors of 28.19 mm, 14.21 mm, and 34.44 mm and the R-squares of 0.9827, 0.9955, and 0.9903, respectively. Moreover, the reliability of the prediction results obtained using this model can be expressed in the flexible prediction intervals (PIs) under different deformation states. The coverage width-based criteria of PIs at 90% nominal confidence are 140.38 mm, 86.61 mm, and 173.68 mm, respectively. In conclusion, the proposed model provides a good basis for developing early warning systems for landslides with step-like deformation characteristics.