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通过频域分析和注意力机制,改进多尺度短期降水预测

Shurui Pan¹, Wei Zhang², Yufang Shen¹

¹Hohai University College of Hydrology and Water Resources, China.

|December 28, 2025

概括

本研究介绍了WE_TransUNet,这是一个用于准确的短期降水预报的新型模型. 它增强了多尺度分析,并减少了精确每小时降雨预测的计算需求.

科学领域:

气象学和大气科学气象学和大气科学
人工智能和机器学习
数据科学和分析数据科学和分析

背景情况:

现有的数据驱动模型在短期降雨事件中扎,缺乏多尺度特征和准确的强度预测.
提高预测准确度往往需要大量的计算资源,这构成了实际挑战.

研究的目的:

开发一种新的短期降水预测模型,以解决现有数据驱动方法的局限性.
量化频率域中的多尺度降水分布特征.
为了能够快速准确地预测每小时降水分布在0-24小时的时间范围内.

主要方法:

与TransUNet混合架构集成一个跨道的多尺度注意力机制 (EMA).
使用波波变换在频域中的多尺度降水分布特征的量化.
开发一种新型的短期降水预测模型,使用降低参数.

主要成果:

WE_TransUNet实现了最佳的预测性能,超过了四种常见的深度学习模型.
平均威胁评分 (TS) 提高了12%,至0.447,平均根平均平方误差 (RMSE) 为0.281.
与传统的CBAM模块相比,EMA模块在捕捉跨尺度的非线性时间序列关系方面表现出卓越的能力.

结论:

拟议的模型有效地从历史降水数据中提取多个尺度的信息,使用更少的参数,减轻深度学习模型的模糊性.
波形变换有助于在预测中保持高频,局部严重的对流现象.
这种方法为短期降水预测提供了一个新的优化策略,提高了准确性和效率.

关键词:

深度学习是一种深度学习.频率域分析频率域分析多个尺度的注意力机制.预测降雨情况的预测.短期预测短期预测

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