Utilizing GNSS data and NRBO-VMD-NRBO-transformer for accurate settlement prediction in Nanshan, Xining

Xiangxiang Hu (), Biao Li, Jixin Yin, Yaya Shi, Bao Song, Dongdong Pang and Baokang Liu
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Xiangxiang Hu: Tianshui Normal University
Biao Li: Tianshui Normal University
Jixin Yin: Xining Institute of Surveying and Mapping
Yaya Shi: Tianshui Normal University
Bao Song: Beijing Institute of Technology
Dongdong Pang: Tianshui Normal University
Baokang Liu: Tianshui Normal University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 13, No 25, 15679-15706

Abstract: Abstract In ground subsidence analysis, the application of Variational Mode Decomposition (VMD) algorithms and Transformer models has been increasingly adopted. However, the tuning of their hyperparameters involves complexity and substantially influences predictive accuracy. To address this issue, an NRBO algorithm is proposed to perform real-time and fine-grained adjustments of hyperparameters. Furthermore, an NRBO–VMD–NRBO–Transformer hybrid model has been developed for the purpose of predicting ground subsidence. Model validation conducted in Xining City indicates that, across long-term, medium-term, and short-term time series analyses, the proposed model outperforms both the standalone Transformer model and the NRBO-optimized VMD–Transformer model. At monitoring point JC23, the R2 values for the respective time series are observed to reach 0.8622, 0.9011, and 0.9268. The NRBO–VMD–NRBO–Transformer model exhibits strong predictive performance in subsidence forecasting. In addition to effectively capturing subsidence trends, the model demonstrates robustness, stability, generalization ability, strong positive correlation, and adaptive parameter tuning capabilities based on data volume.

Keywords: NRBO-VMD-NRBO-transformer; Prediction of land subsidence; GNSS; Prediction method; Deep learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07410-0

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