基于传感器融合的机器学习算法用于气象条件,在港口场景中进行现场预报
Marwan Haruna1,2, Francesco Kotopulos De Angelis1,2, Kaleb Gebremicheal Gebremeskel1
1Consorzio Nazionale Interuniversitario per le Telecomunicazioni, National Laboratory of Photonic Networks & Technologies (PNTLab), Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
准确的短期风力预报对于港口至关重要. 这项研究开发了一个机器学习框架,使用传感器融合进行实时风力预测,XGBoost显示出卓越的性能.
科学领域:
- 海上运营和环境监测
- 机器学习在物流中的应用.
- 物联网 (IoT) 用于港口管理.
背景情况:
- 港口运营日益受到不可预测的天气和环境变化的影响.
- 准确的短期预测对于海事安全和效率至关重要.
- 现有的系统需要提高关键操作的情境意识.
研究的目的:
- 开发一个实时的,多目标的风力状况现在预测框架.
- 使用传感器融合和物联网架构集成异质数据源.
- 评估机器学习模型,以预测风吹速度,持续风速和方向.
主要方法:
- 在利沃诺港使用物联网架构 (oneM2M标准).
- 来自气象站,风力计和船上安装的LiDAR的综合数据.
- 采用了特征级传感器融合,并比较了随机森林,XGBoost,LSTM,TCN,合体神经网络,变压器和卡尔曼波器模型.
主要成果:
- 对于所有风力目标来说,XGBoost 显示了最高的精度 (R2 ≈ 0.999 在单分割中,平均 R2 = 0.9976 在交叉验证中).
- 与深度学习方法相比,合并模型显示了更好的稳定性.
- 传感器融合框架有效地提高了对风速等关键变量的情境意识.
结论:
- 拟议的基于传感器融合的机器学习框架对于实时风力预测非常有效.
- XGBoost是这个多目标预测任务的高性能模型.
- 该框架具有在海上自主水面船 (MASS) 系统和港口决策支持平台中部署的巨大潜力,以提高安全性和运营连续性.
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