大气科学学报2025，Vol.48Issue(4)：618-625,8.DOI:10.13878/j.cnki.dqkxxb.20240720001
川藏铁路路基冻胀气象条件的气候学分析及其预报系统的建立
Research on frost heave prediction of subgrade in the Sichuan-Xizang Railway based on machine learning technology
摘要
Abstract
Accurate prediction of soil freezing depth is critical for ensuring the operational safety of the Sichuan-Tibet Railway(STR),as frozen soil dynamics play a significant role in subgrade deformation.This study devel-oped a high-resolution freezing depth forecasting system for the STR,leveraging a random forest machine learning model combined with multi-factor fusion analysis and integrated numerical meteorological predictions.The system utilizes 11 key predictive variables—including multi-year accumulated temperature,air temperature,humidity,ele-vation,soil composition,and real-time freezing depth—to generate 5 km-resolution gridded forecasts of freezing depth up to 7 days in advance.Based on an extensive analysis of meteorological risk factors and 40 years(1980-2021)of climate data from the Xizang region,three key findings emerged:1)The spatial distribution of frozen soil in the Tibet section of the STR is strongly corelated with altitude.Seasonally frozen soil—characterized by winter freezing and summer thawing—is primarily distributed above 4 000 m in regions such as Bangda Grass-land,Guoging(Baxoi County),and Lajiu(Lhorong County).These areas exhibit annual mean ground tempera-tures below-5.0 ℃,with seasonal freezing depths ranging from 2.0 to 3.5 m,significantly impacting embank-ment sections,particularly in the Bangda Grassland.2)Over the past four decades,the region has experienced a warming and wetting climate trend,accompanied by declining wind speeds.The maximum annual freezing depth has decreased at a rate of 0.37-11.55 cm per decade,with the greatest reduction at Qamdo Station and the smal-lest at Nyingchi Station.3)The random forest-based freezing depth prediction model demonstrated high accuracy and reliability.Validation using field measurements yielded a prediction accuracy of 96％,a TS score of 0.96,a 3％missed detection rate,and zero false alarms.The model achieved a R2 value of 0.74 and a RMSE(root mean squared error)of 66.34 cm,indicating strong consistency between predicted and observed values.关键词
川藏铁路/机器学习/冻结深度预报系统Key words
Sichuan-Xizang Railway/machine learning/freezing depth prediction model引用本文复制引用
德庆卓嘎,拉珍,次仁,匡秋明..川藏铁路路基冻胀气象条件的气候学分析及其预报系统的建立[J].大气科学学报,2025,48(4):618-625,8.基金项目
2020年中央引导地方项目(XZ202001YD0006C) （XZ202001YD0006C）
中国气象局公共气象服务中心创新基金项目(M2022009) （M2022009）
国家自然科学基金项目(11775180) （11775180）
