Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 18
Articles | Volume 19, issue 18
https://doi.org/10.5194/gmd-19-9035-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-9035-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 18
Development and technical paper
 | 
24 Sep 2026
Development and technical paper |  | 24 Sep 2026

A hybrid method for winter road surface temperature prediction using improved LSTMs and stacking-based ensemble learning

Wanting Li, Linyi Zhou, Xianghua Wu, Yuanhong Guan, Yuanhao Guo, Kun Chen, Weiqi Huang, and Wenqian Zhao

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Short summary
Accurate winter road surface temperature prediction prevents icy‑road accidents. Existing methods either need rare pavement parameters or miss local weather patterns and long‑term trends. This study combines two deep‑learning models to analyse four winters of Jiangsu data. It achieves lower prediction errors in general and outperforms common models at 1, 3  and 6 h horizons. Station‑based features beat external reanalysis data.
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