Abstract：In order to accurately grasp the development law of landslide deformation, based on landslide deformation monitoring results, we construct a landslide early warning prediction model. We first carry out multifractal characteristics analysis of landslide deformation data using the MF-DFA model, and use M-K analysis to study landslide early warning by constructing dual criteria, namely Δa index criterion and Δf(a) index criterion. Secondly, based on the separation and processing of landslide deformation data by ensemble empirical mode decomposition method, we use the GOA-RNN-CT model to realize the sub item combination prediction of landslide deformation. The results show that the value of h(q) decreases with the decrease of q value of wave function, indicating that landslide deformation data has multifractal characteristics. Through the study of early warning classification, we conclude that the landslide early warning level is grade II, that is, the landslide deformation tends to develop in an unfavorable direction. At the same time, through the deformation prediction analysis, we conclude that the sub item combination prediction has better effect and stability in landslide deformation prediction, and the extrapolation prediction results show that the landslide deformation will continue to increase. Finally, the combined response of multifractal characteristics results and deformation prediction analysis results show that the existing early warning level of landslide is relatively unfavorable, and the subsequent deformation will further increase and tend to further instability. We suggest taking necessary prevention and control measures for landslide.
LEI Heng,ZHOU Xiaolan,WANG Yongqiang. Research on Landslide Early Warning and Prediction Based on Combined Response of Multifractal Characteristics and Sub Item Prediction[J]. jgg, 2022, 42(9): 885-891.