IPSO-ELM intelligent prediction of landslide displacement in complex and unstable area of karst landform

Abstract

In southern China, the karst landform areas possess a complex geological and topographic environment, a fragile ecosystem, poor surface stability, and frequent occurrences of landslides and other geological disasters. To effectively monitor and predict such events, it is crucial to process landslide monitoring data and establish reliable prediction models. This paper presents an IPSO-ELM displacement prediction model that integrates the improved particle swarm optimization algorithm (IPSO) and extreme learning machine (ELM). The proposed coupling model predicts decomposed displacement subsequences individually, which are then reconstructed to obtain the total displacement prediction value. In this study, displacement monitoring data from a typical landslide in the karst landform area between 2007 and 2012 were selected. Various prediction and verification scenarios were established to validate the accuracy and stability of the prediction model. The MAPE of the IPSO-ELM model is 0.18%, which outperforms the ELM and BPNN models with MAPEs of 0.56% and 0.65%, respectively, in predicting landslide displacement in karst landform areas. This study provides a solid theoretical foundation and practical value for landslide displacement prediction.