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黄佳威, 王永前, 吴小娟. 2024. 顾及地形差异的高速公路结冰预报模型研究[J]. 大气科学, 48(4): 1−12. DOI: 10.3878/j.issn.1006-9895.2305.22098
引用本文: 黄佳威, 王永前, 吴小娟. 2024. 顾及地形差异的高速公路结冰预报模型研究[J]. 大气科学, 48(4): 1−12. DOI: 10.3878/j.issn.1006-9895.2305.22098
HUANG Jiawei, WANG Yongqian, WU Xiaojuan. 2024. Expressway Icing–Prediction Model Considering Terrain Differences [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 48(4): 1−12. DOI: 10.3878/j.issn.1006-9895.2305.22098
Citation: HUANG Jiawei, WANG Yongqian, WU Xiaojuan. 2024. Expressway Icing–Prediction Model Considering Terrain Differences [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 48(4): 1−12. DOI: 10.3878/j.issn.1006-9895.2305.22098

顾及地形差异的高速公路结冰预报模型研究

Expressway Icing–Prediction Model Considering Terrain Differences

  • 摘要: 基于四川省158个逐日气象观测站点数据以及2018~2020年道路气象灾害资料构建数据集,利用皮尔逊相关系数对道路结冰同温度类、湿度类、降水类及其日变化复合特征等14个气象因子进行相关性分析,探索平原和山区地形下道路结冰模式差异,通过数据集构建不同地形下的Logistic结冰预警模型,同时利用在线性模块中添加L1惩罚项的Lasso(Least Absolute Shrinkage and Selection Operator)原理、基于正交变换减少数据维度的主成分分析(Principle Component Analysis,简称PCA)和基于显著性对变量进行筛选的逐步回归(Stepwise Regression,简称SR)三种方法对模型进行降维改进,并采用五折交叉验证减小偶然误差。研究结果可为复杂地形的道路结冰预警预报提供参考。结论表明:(1)低温均为道路结冰发生的首要条件,相对湿度因子与结冰的相关系数差异说明不同地形下水汽供给模式存在区别。(2)Lasso-Logistic模型在平原和山区地形下预测效果均最佳,平均准确率分别为87.67%和82.62%;SR-Logistic的平均准确率为85.77%和80.64%;PCA-Logistic的准确率均值为84.09%和77.44%。(3)引入气象因子随时间变化的复合特征能够提高模型的预测准确率,添加复合特征的Lasso-Logistic、SR-Logistic和PCA-Logistic模型准确率平均提升了3.68%、3.00%和3.00%。(4)所有模型在平原地区的表现都好于山区,Lasso、SR和PCA Logistic模型的平均准确率分别高出5.06%、5.13%和6.65%。

     

    Abstract: Herein, a dataset is constructed using the data from 158 daily meteorological observation stations of Sichuan Province and road meteorological disaster data from 2018 to 2020. Using Pearson’s correlation coefficient, correlation analysis is performed on 14 meteorological factors, including temperature, humidity, precipitation, and their daily variation compound features, to examine the differences in road icing patterns between plain and mountainous terrains. Logistic icing-warning models are built for various terrains using the dataset. Dimensionality reduction is employed to enhance the models by employing three methods: method of least absolute shrinkage and selection operator (LASSO) with the L1 penalty term in the linear module, principal component analysis (PCA) based on orthogonal transformation to reduce data dimensions, and stepwise regression (SR) based on significance for variable screening. Random errors are reduced using five-fold cross-validation. The research results can provide a reference for road icing warnings and forecasting for complex terrains. The following conclusions can be drawn. (1) Low temperature is the primary condition for the occurrence of road icing, and the difference in correlation coefficients between relative humidity factors and icing demonstrates different patterns of water vapor supply in different terrains. (2) The LASSO-logistic model exhibits the best prediction performance for both plain and mountainous terrains, with average accuracies of 87.67% and 82.62%, respectively; the average accuracies of the SR-logistic model are 85.77% and 80.64%, respectively, while those of the PCA-logistic model are 84.09% and 77.44%, respectively. (3) Introducing compound features of meteorological factors changing over time can enhance the model accuracy, and the average accuracies of the Lasso-, SR-, and PCA-logistic models with added compound features are increased by 3.68%, 3.00%, and 3.00%, respectively. (4) All models performed better for plain areas than for mountainous areas, with an average accuracy of 5.06%, 5.13%, and 6.65% higher for Lasso-, SR-, and PCA-Logistic models.

     

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