Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

• Published in: PloS one Vol. 19; no. 3; p. e0298524
• Main Authors: Zhu, Shengchao, Qin, Yongjun, Meng, Xin, Xie, Liangfu, Zhang, Yongkang, Yuan, Yangchun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.03.2024; Public Library of Science (PLoS)
• Subjects: Computer and Information Sciences; Decomposition method; Engineering and Technology; Forecasts and trends; Industry; Long Interspersed Nucleotide Elements; Methods; Neural networks; Neural Networks, Computer; Physical Sciences; Railroads; Reproducibility of Results; Research and Analysis Methods; Signal processing; Surfaces, Deformation of; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0298524

The uneven settlement of the surrounding ground surface caused by subway construction is not only complicated but also liable to cause casualties and property damage, so a timely understanding of the ground settlement deformation in the subway excavation and its prediction in real time is of practical significance. Due to the complex nonlinear relationship between subway settlement deformation and numerous influencing factors, as well as the existence of a time lag effect and the influence of various factors in the process, the prediction performance and accuracy of traditional prediction methods can no longer meet industry demands. Therefore, this paper proposes a surface settlement deformation prediction model by combining noise reduction and attention mechanism (AM) with the long short-term memory (LSTM). The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) methods are used to denoise the input original data and then combined with AM and LSTM for prediction to obtain the CEEMDAN-ICA-AM-LSTM (CIAL) prediction model. Taking the settlement monitoring data of the construction site of Urumqi Rail Transit Line 1 as an example for analysis reveals that the model in this paper has better effectiveness and applicability in the prediction of surface settlement deformation than multiple prediction models. The RMSE, MAE, and MAPE values of the CIAL model are 0.041, 0.033 and 0.384%; R 2 is the largest; the prediction effect is the best; the prediction accuracy is the highest; and its reliability is good. The new method is effective for monitoring the safety of surface settlement deformation.