SDIPPWV:基于阶段性分解-整合-预测的新型混合预测模型避免了未来的信息泄露,以预测来自GNSS观测的沉式水蒸气
Fanming Wu1, Dengao Li2, Jumin Zhao3
1College of Computer Science and Technology, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China.
The Science of the total environment
|May 11, 2024
概括
这项研究引入了一种用于预测水蒸气含量的新方法,提高了天气预报的准确性. 逐步分解-整合-预测沉式水蒸气 (SDIPPWV) 模型克服了当前混合模型中的数据泄漏问题.
科学领域:
- 气象学和大气科学 气象学和大气科学
- 地质测量和卫星导航
背景情况:
- 准确的水蒸气预测对于大雨预测和人工降水操作至关重要.
- 现有的混合模型在后续重建过程中遭受未来数据泄露和错误积累的困扰.
研究的目的:
- 为解决数据泄漏问题和提高预测准确性,提出一种新的逐步分解-整合-预测沉式水蒸气 (SDIPPWV) 机制.
- 通过使用全球导航卫星系统 (GNSS) 数据,增强可降水蒸气 (PWV) 的预测.
主要方法:
- 高精度的PWV序列从GNSS观测文件中获取.
- 在固定尺寸窗口中使用基于Loess (STL) 的季节趋势分解的逐步分解过程.
- 使用1D卷积神经网络-双向长短期记忆 (1D CNN-BiLSTM) 模型进行分解子序列和预测的特征集成.
主要成果:
- 与八个GNSS站的单一和其他混合预测模型相比,SDIPPWV模型表现得更好.
- 获得的平均RMSE为0.2146毫米,MAE为0.1132毫米,MAPE为1.29%,R2为0.9998.8.
- 成功地缓解了先前模型固有的数据泄漏问题.
结论:
- 拟议的SDIPPWV模型显著提高了水蒸气含量预测的准确性.
- 该方法为气象预报和相关应用提供了可靠的方法.
- 将GNSS数据与先进的深度学习技术集成,可以提供可靠的水蒸气估计.
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