在江苏省基于GCN-GRU模型的可见度预测
Huansang Chen1, Yihang Xu2, Zhiqiu Gao3
1School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Scientific reports
|June 1, 2024
概括
准确的短期可见度预测对于道路安全至关重要. 一个新的GCN-GRU模型,考虑到湿度和污染物度,比现有方法显著改善了6小时的预测.
科学领域:
- 环境科学 环境科学
- 气象学 天气学
- 运输工程 运输工程
背景情况:
- 低能见度的天气条件显著增加了交通事故的风险.
- 准确的可见度预测对于减轻运输危险和确保公共安全至关重要.
研究的目的:
- 开发和评估用于短期可见性预测的先进模型.
- 确定影响江苏省可见度的关键气象和环境因素.
主要方法:
- 收集了江苏省2017-2018年气象和环境监测数据.
- 利用最大相关性最小冗余性 (mRMR) 算法来识别影响可见性的关键因素.
- 开发了一种混合图形卷积网络和门式循环单元 (GCN-GRU) 模型,用于时空预测.
主要成果:
- 湿度和度被确定为影响可见度的主要因素.
- 与独立的GRU和其他机器学习模型相比,GCN-GRU模型在6小时可见度预测中表现出卓越的性能.
- 在相关系数 (CORR),根平均平方误差 (RMSE),平均绝对百分比误差 (MAPE) 和平均绝对误差 (MAE) 中,GCN-GRU取得了显著的改进.
结论:
- GCN-GRU模型为短期可见性预测提供了一个强大而准确的方法.
- 整合空间 (GCN) 和时间 (GRU) 分析可以提高预测的准确性.
- 这项研究为改善低可见度条件下的交通安全管理提供了宝贵的见解.
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