Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2020.105281 |
Improving ECMWF short-term intensive rainfall forecasts using generative adversarial nets and deep belief networks | |
Huosheng Xie, Lidong Wu, Wei Xie, Qing Lin, ... Yongjing Lin | |
2020-09-25 | |
发表期刊 | Atmospheric Research |
出版年 | 2020 |
英文摘要 | Short-term intensive rainfall (3-h rainfall amount > 30 mm) is a destructive weather phenomenon that is poorly predicted using traditional forecasting methods. In this study, we propose a model using European Center for Medium-Range Weather Forecasts (ECMWF) data and a machine learning framework to improve the ability of short-term intensive rainfall forecasting in Fujian Province, China. ECMWF forecast data and ground observation station data (2015–2018) were interpolated using a radial basis function, outliers were processed, and the data were blocked according to the monthly cumulative rainfall and forecast window. Subsequently, the box difference index was used to select features for each data block. As short-term intensive rainfall events are rare, a data processing method based on the K-means and generative adversarial nets was used to address data imbalances in the rainfall distribution. Finally, focal loss object detection was combined with a deep belief network to construct the short-term intensive rainfall classification model. The results show that the data preprocessing method and resampling method used in this study were effective. Furthermore, the classification model was superior to other machine learning methods for predicting short-term intensive rainfall. |
领域 | 地球科学 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/296302 |
专题 | 地球科学 |
推荐引用方式 GB/T 7714 | Huosheng Xie, Lidong Wu, Wei Xie, Qing Lin, ... Yongjing Lin. Improving ECMWF short-term intensive rainfall forecasts using generative adversarial nets and deep belief networks[J]. Atmospheric Research,2020. |
APA | Huosheng Xie, Lidong Wu, Wei Xie, Qing Lin, ... Yongjing Lin.(2020).Improving ECMWF short-term intensive rainfall forecasts using generative adversarial nets and deep belief networks.Atmospheric Research. |
MLA | Huosheng Xie, Lidong Wu, Wei Xie, Qing Lin, ... Yongjing Lin."Improving ECMWF short-term intensive rainfall forecasts using generative adversarial nets and deep belief networks".Atmospheric Research (2020). |
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