基于数值天气预报与机器学习技术的道路气象状况预测  

Road Weather Condition Prediction Based on Numerical Weather Prediction and Machine Learning Technology

作　　者：蒲秀姝 刘新超 宋怡轩 郭荣 郭洁 PU Xiushu;LIU Xinchao;SONG Yixuan;GUO Rong;GUO Jie(Sichuan Meteorological Service Center,Chengdu 610072,China;Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province,Chengdu 610000,China;College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730000,China)

机构地区：[1]四川省气象服务中心,四川成都610072 [2]高原与盆地暴雨旱涝灾害四川省重点实验室,四川成都610000 [3]西北师范大学计算机科学与工程学院,甘肃兰州730000

出　　处：《热带气象学报》2024年第6期993-1004,共12页Journal of Tropical Meteorology

基　　金：高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金青年专项(SCQXKJQN202218);中国气象局西南区域气象中心创新团队基金(XNQYCXTD-202203)共同资助。

摘　　要：道路气象状况与交通安全密切相关,路面湿滑、结冰易引发行车事故,因此需要实现准确且及时的道路气象状况预测。利用雅康高速路段内3个地面观测点的道路气象状况观测数据,以及对应区域的连续24 h数值天气预报数据,构建决策树模型,建立数值天气预报结果与多种道路气象状况类别之间的对应关系,实现未来连续24 h的道路气象状况预测。结果表明,针对3个地面观测点的5类道路气象状况,在交叉验证实验中,所提出模型的平均准确率为89.79%,在外推实验中,模型对未来第6 h预测的平均准确率为64.73%,未来第12h预测为77.30%,未来第18 h预测为80.19%,未来第24 h预测为70.41%。研究方法可以有效实现连续空间覆盖、长时间的道路气象状况预测,为交通运输安全、公众出行决策、气象预报服务等方面提供重要参考信息。Road weather conditions are closely related to traffic safety,as slippery and icy roads can easily lead to accidents.Therefore,accurate and timely predictions of road weather conditions are essential.The data used in the present study included the observation data of road weather conditions from three ground observation stations along the Yakang highway and the 24-hour numerical weather prediction data for the corresponding area.Based on a decision tree model,the corresponding relationship between numerical weather prediction results and various types of road weather conditions was established,enabling predictions of road weather conditions for the next 24 hours.The results show that,for the five types of road weather conditions at three ground observation stations,the average accuracy of cross-validation for our proposed model was 89.79%.In the extrapolation experiment,the average accuracy of prediction for the next 6 hours was 64.73%,for the next 12 hours was 77.30%,for the next 18 hours was 80.19%,and for the next 24 hours was 70.41%.Our research method effectively achieved continuous spatial coverage and long-term prediction of road weather conditions,providing important reference information for traffic safety,public travel decision-making,and weather forecasting services.

关 键 词：道路气象状况 预测模型 决策树 机器学习 气象服务 

分 类 号：P456[天文地球—大气科学及气象学]