ORIGINAL RESEARCH article
Front. Soil Sci.
Sec. Pedometrics
Volume 5 - 2025 | doi: 10.3389/fsoil.2025.1642004
Variations in maximum freezing depth in Northeast China from 1975 to 2024 using a machine learning model
Provisionally accepted
- School of Transportation, Kashgar University, Kashgar, China
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A freezing depth prediction model was constructed using machine learning, incorporating comprehensive data from ground meteorological monitoring stations and remote sensing reanalysis data. The maximum freezing depth (MFD) of seasonally frozen ground (SFG) in Northeast China was systematically analyzed from 1975 to 2024. The simulation results from the machine learning model (MLM) indicated that the MFD of SFG in Northeast China displayed a decreasing trend over the past 50 years, with an average rate of change of -8.54 cm per decade. The average maximum freezing depths (AMFDs) in Northeast China for each decade were: 136.71 cm (1975−1984), 131.96 cm (1985−1994), 123.07 cm (1995−2004), 110.82 cm (2005−2014), and 104.58 cm (2015−2024). The area occupied by each AMFD interval in Northeast China over the past 50 years increased in regions with freezing depths <160 cm. The area with freezing depths >160 cm displayed a decreasing trend. The results not only reveal the impact of climate change on freezing depths, but also provide a scientific basis for environmental management and ecological protection in frozen ground areas. Changes in freezing depth directly affect many sectors such as agriculture, construction, and transportation, making accurate prediction essential for developing climate adaptation strategies. Considering the lack of data regarding the MFD of SFG in Northeast China for the past 50 years, the MLM provided an effective method for predicting changes in MFD using meteorological data and remote sensing reanalysis data.
Keywords: seasonally frozen ground, Northeast China, machine learning, Average maximum freezing depth, Remote sensing reanalysis data
Received: 05 Jun 2025; Accepted: 25 Jul 2025.
Copyright: © 2025 Wang, Tuerhong, Maimaitituersun and Ning. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zuo-Jun Ning, School of Transportation, Kashgar University, Kashgar, China
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