A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction

Wu, Jinhua; Cao, Chengdu; Fei, Liang; Han, Xiangyang; Wang, Yuli; Chan, Ting On

doi:10.3390/s25196041

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Open AccessArticle

A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction

by

Jinhua Wu

¹

,

Chengdu Cao

¹,

Liang Fei

^1,*,

Xiangyang Han

²

,

Yuli Wang

³ and

Ting On Chan

²

¹

China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China

²

School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China

³

Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China

^*

Author to whom correspondence should be addressed.

Sensors 2025, 25(19), 6041; https://doi.org/10.3390/s25196041

Submission received: 20 July 2025 / Revised: 28 September 2025 / Accepted: 29 September 2025 / Published: 1 October 2025

(This article belongs to the Special Issue Structural Health Monitoring and Smart Disaster Prevention)

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Abstract

Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid predicting framework that integrates Past Decomposable Mixing with an inverted Transformer architecture (PDM-iTransformer). The PDM module decomposes the original sequence into multi-resolution trend and seasonal components, using structured bottom-up and top-down mixing strategies to enhance feature representation. The iTransformer then models each variable’s time series independently, applying cross-variable self-attention to capture latent dependencies and using feed-forward networks to extract local dynamic features. This design enables simultaneous modeling of long-term trends and short-term fluctuations. Experimental results on GNSS monitoring data demonstrate that the proposed method significantly outperforms traditional models, with R² increased by 16.2–48.3% and RMSE and MAE reduced by up to 1.33 mm and 1.08 mm, respectively. These findings validate the framework’s effectiveness and robustness in predicting landslide displacement under complex terrain conditions.

Keywords: GNSS displacement monitoring; time series decomposition; attention-based neural networks; multiscale time series modeling; landslide displacement prediction

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MDPI and ACS Style

Wu, J.; Cao, C.; Fei, L.; Han, X.; Wang, Y.; Chan, T.O. A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction. Sensors 2025, 25, 6041. https://doi.org/10.3390/s25196041

AMA Style

Wu J, Cao C, Fei L, Han X, Wang Y, Chan TO. A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction. Sensors. 2025; 25(19):6041. https://doi.org/10.3390/s25196041

Chicago/Turabian Style

Wu, Jinhua, Chengdu Cao, Liang Fei, Xiangyang Han, Yuli Wang, and Ting On Chan. 2025. "A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction" Sensors 25, no. 19: 6041. https://doi.org/10.3390/s25196041

APA Style

Wu, J., Cao, C., Fei, L., Han, X., Wang, Y., & Chan, T. O. (2025). A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction. Sensors, 25(19), 6041. https://doi.org/10.3390/s25196041

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A Hybrid Framework Integrating Past Decomposable Mixing and Inverted Transformer for GNSS-Based Landslide Displacement Prediction

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