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STVMamba:降水现在用时空预测模型进行预测.
Maoyang Zou1, Longrui Wen1, Yuanyuan Huang1
1School of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China.
Scientific reports
|July 2, 2025
概括
一个新的时空视觉马巴 (STVMamba) 模型通过捕捉远程依赖来提供高效和准确的降雨预测. 它在气象局的各种数据集上优于现有的方法.
科学领域:
- 气象学 天气学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 轻量级降雨现在预测模型对于气象局至关重要.
- 现有的深度学习模型 (循环,卷积,变压器) 在效率和捕获远程依赖性方面存在局限性.
研究的目的:
- 介绍空间时间视觉巴 (STVMamba),这是一个新型模型,用于高效和准确的降水现在预测.
- 克服现有的深度学习方法在降雨预测方面的局限性.
主要方法:
- 开发了STVMamba,这是一个具有高并行计算效率和线性时间复杂性的时空预测模型.
- 使用空间时空选择性扫描 (STSS) 进行全球空间时空关系和空间时空深度可分离卷积 (STDSConv) 进行本地关系.
- 采用双层架构来学习跨多个空间尺度的时空关系.
主要成果:
- 在四川雷达回声数据集 (MSE,SSIM,CSI-10) 上,STVMamba 实现了最先进的性能.
- 在HKO-7雷达回声数据集 (MSE,CSI-10,CSI-20) 上表现优于现有模型.
- 在IMERG卫星数据集 (SSIM,CSI-0.5) 上表现出卓越的结果,在不同条件下显示出稳定性.
结论:
- STVMamba有效地解决了以前用于降雨预测的深度学习模型的局限性.
- 该模型表现出高效率,在捕获远程依赖性方面表现出强的表现,以及跨不同数据集的稳定性.
- 在气象应用中,STVMamba代表了降水预测技术的重大进步.
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