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The improved mountain gazelle optimizer for spatiotemporal support vector regression: a novel method for railway subgrade settlement prediction integrating multi-source information

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Abstract

The uneven settlement of railway subgrades not only affects the comfort of train operations but, in extreme cases, may compromise operational safety. As a result, accurately predicting subgrade settlement is crucial for maintaining both safety and operational efficiency. This study introduces an Improved Mountain Gazelle Optimizer for the Spatiotemporal Support Vector Regression (IMGO-STSVR) model, which effectively predicts railway subgrade settlement. Data are collected using Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology in combination with a multi-source environmental monitoring system. The proposed improvement to the Mountain Gazelle Optimizer (IMGO) enhances the model’s optimization capabilities, while the Support Vector Regression model is improved by the constructed spatiotemporal kernel function (STSVR). Experimental results demonstrate that the IMGO-STSVR model achieves high accuracy and stability across various experimental sites. This method provides valuable insights for predicting subgrade settlement in the railway industry, aiding in the early identification of potential risks, optimizing maintenance strategies, and ensuring the safe and efficient operation of rail transport.

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Data Availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to extend our sincere gratitude to the Kuitun Public Works Department of China Railway Urumqi Bureau Group Co. for generously providing the experimental site and facilitating the data collection process. Additionally, we acknowledge with appreciation the significant financial and institutional support from the Industrial Support Program for Universities in Gansu Province (2023CYZC-32), the Science and Technology Program of the National Railway Group (N2023G064), the Science and Technology Guidance Program of Gansu Province (2020-61-14), Gansu Provincial Science and Technology Program (21ZD4WA018, 24RCKA013), and the Gansu Province Graduate ”Innovation Star” Program (2025CXZX-666, 2025CXZX-664). This invaluable support has been instrumental in the successful completion of our research.

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, 730070, China

    Guangwu Chen, Shilin Zhao, Peng Li, Shilin Wang & Xin Zhou

  2. Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province, Lanzhou Jiaotong University, Lanzhou, Gansu, 730070, China

    Guangwu Chen, Shilin Zhao, Peng Li, Shilin Wang & Xin Zhou

  3. School of Digital Media, Lanzhou University of Arts and Sciences, Lanzhou, Gansu, 730010, China

    Shilin Zhao

  4. School of Cyber-Physical and Control Systems, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, 195251, Russia

    Vyacheslav Potekhin

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Chen, G., Zhao, S., Li, P. et al. The improved mountain gazelle optimizer for spatiotemporal support vector regression: a novel method for railway subgrade settlement prediction integrating multi-source information. Appl Intell 55, 502 (2025). https://doi.org/10.1007/s10489-025-06397-2

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