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基于ConvLSTM和SmaAT-UNet的短期和即将发生的降雨预测模型

Yuanyuan Liao¹, Shouqian Lu¹, Gang Yin²

¹School of Computer Science and Technology, Xinjiang University, Urumqi 830049, China.

Sensors (Basel, Switzerland)

|June 19, 2024

概括

深度学习模型,ConvLSTM和SmaAT-UNet,通过雷达图像推断来增强短期降水预测. 与ConvLSTM相比,SmaAT-UNet表现出更高的准确性,与传统方法相比,改善了降雨预测.

科学领域:

气象学和大气科学气象学和大气科学
人工智能的人工智能
计算机科学计算机科学

背景情况:

传统的短期降水预报依赖于统计和数值模型,经常表现出不稳定性和重大错误.
雷达图像外推提供了一个替代方案,但需要先进的空间时间数据处理技术.

研究的目的:

研究深度学习模型的有效性,特别是ConvLSTM和SmaAT-UNet,通过雷达图像推断来增强短期降水预测.
将ConvLSTM和SmaAT-UNet的性能与传统预测方法进行比较.

主要方法:

开发和实施ConvLSTM,一种混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型,用于时空数据处理.
通过整合CBAM注意力机制和深度可分离卷积来改进特征提取和效率,将UNet架构增强到SmaAT-UNet中.

主要成果:

无论是ConvLSTM还是SmaAT-UNet,都在短期降水预测方面表现出强大的预测能力.
SmaAT-UNet的准确性高于ConvLSTM,特别是随着预测时间的延长.
检测概率 (POD) 和关键成功指数 (CSI) 等绩效指标自然会随着预测时间的延长而下降.

结论:

基于深度学习的雷达图像外推显著提高了与统计和数值模型相比,短期降水预测的准确性.
SmaAT-UNet为降水预测提供了一种有希望的,高度准确和有弹性的方法,其性能优于ConvLSTM.

关键词:

这就是ConvLSTM.这就是SmaAT-UNet.深度学习是一种深度学习.雷达图像的取值方法短期降雨预报短期降雨预报

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