IPSO-ELM intelligent prediction of landslide displacement in complex and unstable area of karst landform - Qiao, Junwei; Zhang, Yu; Li, Weibo; Tan, Jieqing (Frontiers in Earth Science, 2023)
Karst of China are widely distributed in mountainous areas with complex geological and topographical environments, fragile ecosystems and poor surface stability. The typical landslide disaster in this paper is located in the area with the most active karst landform in China. The karst activities on the underground surface are very frequent, resulting in the very active micro-movement of the underground surface and frequent geological disasters. The prediction of landslide displacement in this area is helpful to predict natural disasters such as landslides. The processing of landslide monitoring data and the establishment of a prediction model are the basis of landslide displacement prediction. This paper combines the improved particle swarm optimization algorithm (IPSO) and extreme learning machines to build IPSO-ELM displacement prediction model. The coupling model is used to predict the decomposed displacement subsequences respectively, and then the total displacement prediction value is obtained by reconstructing the prediction results of the subsequences. In this study, the displacement monitoring data of a typical landslide in the karst landform area from 2007 to 2012 were selected, and different prediction and verification scenarios were set up to verify the validity and stability of the prediction model. MAPE of IPSO-ELM model is 0.18%, while the MAPE of ELM and BPNN models are 0.56% and 0.65%, indicating that the IPSO-ELM model is superior to ELM and BPNN models in predicting landslide displacement. This paper provides a good theoretical basis and engineering application value for landslide displacement prediction in the karst landform area.