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MLAM:基于时空空间序列神经网络的雷达推断的多层注意模块.

Shengchun Wang¹, Tianyang Wang¹, Sihong Wang¹

¹College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.

Sensors (Basel, Switzerland)

|October 14, 2023

概括

本研究引入了一种多层注意模块 (MLAM),以使用卷积循环神经网络 (ConvRNNs) 改进降水现在播放. 增强的ConvRNNs更好地预测高强度的回声和长期依赖性,以便更准确的雷达外推.

关键词:

注意力机制注意力机制卷积循环神经网络是卷积循环神经网络.降水现在播放降水雷达回声外加推算雷达回声外加推算

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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:

降水现在广播通常使用雷达回声外推.
卷积循环神经网络 (ConvRNNs) 显示出希望,但在预测高强度回声和长期特征依赖方面扎.

研究的目的:

为了解决ConvRNN在雷达回声外推中的局限性.
为了提高降水预测的准确性和时间一致性.

主要方法:

开发了一个嵌入式多层注意模块 (MLAM).
MLAM提高了对关键回声区域和长期时空特征的关注.
在HKO-7和HMB雷达数据集上进行了实验.

主要成果:

与MLAM集成的ConvRNNs与标准ConvRNNs相比显示出更高的性能.
拟议的方法显示了不同强度的回声预测的改进.
观察到捕捉长期特征依赖性的增强能力.

结论:

嵌入式多层注意模块 (MLAM) 有效地增强了ConvRNNs用于降水现在预测.
MLAM集成导致更准确,更可靠的雷达回声抽取.
这种方法在气象预报技术方面取得了重大进展.