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Received:February 02, 2024 Revised:February 23, 2024
Received:February 02, 2024 Revised:February 23, 2024
中文摘要: 针对传统降水预测方法的局限性, 提出了一种融合多源数据的深度学习短时降水预测模型MSF-Net. 在GPM历史降水数据的基础上融合了ERA5气象数据、雷达数据和DEM数据. 利用气象特征提取模块学习多源数据的气象特征, 通过注意力融合预测模块进行特征融合并实现短时降水预测. 将MSF-Net的降水预测结果与多种人工智能方法进行对比, 实验结果表明, MSF-Net模型的风险评分TS和偏差评分Bias最优, 表明其可以在6 h的预测时效内提升数据驱动降水预测的效果.
Abstract:This study proposes a deep learning model for short-term precipitation forecasting, called MSF-Net, to address the limitations of traditional methods. This model integrates multi-source data, including GPM historical precipitation data, ERA5 meteorological data, radar data, and DEM data. A meteorological feature extraction module is employed to learn the meteorological features of the multi-source data. An attention fusion prediction module is used to achieve feature fusion and short-term precipitation forecasting. The precipitation forecasting results of MSF-Net are compared with those of various artificial intelligence methods. Experimental results indicate that MSF-Net achieves optimal threat score (TS) and bias score (Bias). This suggests that it can enhance the effectiveness of data-driven precipitation forecasting within a 6 h prediction horizon.
keywords: deep learning short-term precipitation prediction attention mechanism data fusion data-driven
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基金项目:国家重点研发计划(2021YFB2901900); 江苏省研究生科研与实践创新计划(SJCX23_0407)
引用文本：
夏景明,戴如晨,谈玲.融合多源数据的深度学习短时降水预测.计算机系统应用,2024,33(8):123-131
XIA Jing-Ming,DAI Ru-Chen,TAN Ling.Deep Learning for Short-term Precipitation Prediction Integrating Multi-source Data.COMPUTER SYSTEMS APPLICATIONS,2024,33(8):123-131
夏景明,戴如晨,谈玲.融合多源数据的深度学习短时降水预测.计算机系统应用,2024,33(8):123-131
XIA Jing-Ming,DAI Ru-Chen,TAN Ling.Deep Learning for Short-term Precipitation Prediction Integrating Multi-source Data.COMPUTER SYSTEMS APPLICATIONS,2024,33(8):123-131
