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| DOI | 10.3390/w16010097 |
| Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on Clustering | |
| 发表日期 | 2024 |
| EISSN | 2073-4441 |
| 起始页码 | 16 |
| 结束页码 | 1 |
| 卷号 | 16期号:1 |
| 英文摘要 | Recently, Asia has experienced significant damage from extreme precipitation events caused by climate change. Improving the accuracy of quantitative precipitation forecasts over wide regions is essential to mitigate the damage caused by precipitation-related natural disasters. This study compared the predictive performances of a global model trained on the entire dataset and a clustered model that clustered precipitation types. The precipitation prediction model was constructed by combining convolutional long short-term memory with a U-Net structure. Research data consisted of precipitation events recorded at 10 min intervals from 2017 to 2021, utilizing radar data covering the entire Korean Peninsula. The model was trained on radar precipitation data from 30 min before the current time (t - 30 min, t - 20 min, t - 10 min, and t - 0 min) to predict the precipitation after 10 min (t + 10 min). The prediction performance was assessed using the root mean squared error and mean absolute error for continuous precipitation data and precision, recall, F1 score, and accuracy for the presence or absence of precipitation. The research findings indicate that, with sufficient training data for each precipitation type, models trained on clustered precipitation types outperform those trained on the entire dataset, particularly for predicting high-intensity precipitation events. |
| 英文关键词 | convolutional long short-term memory with U-Net structure; clustered precipitation types; high-intensity precipitation; quantitative precipitation forecast |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Water Resources |
| WOS类目 | Environmental Sciences ; Water Resources |
| WOS记录号 | WOS:001140554700001 |
| 来源期刊 | WATER
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/295815 |
| 作者单位 | Daegu University; Korea Institute of Civil Engineering & Building Technology (KICT); Korea Hydro & Nuclear Power |
| 推荐引用方式 GB/T 7714 | . Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on Clustering[J],2024,16(1). |
| APA | (2024).Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on Clustering.WATER,16(1). |
| MLA | "Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on Clustering".WATER 16.1(2024). |
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