DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ko, Jihoon | ko |
dc.contributor.author | Lee, Kyuhan | ko |
dc.contributor.author | Hwang, Hyunjin | ko |
dc.contributor.author | Shin, Kijung | ko |
dc.date.accessioned | 2023-09-14T11:00:15Z | - |
dc.date.available | 2023-09-14T11:00:15Z | - |
dc.date.created | 2023-09-14 | - |
dc.date.issued | 2022-11 | - |
dc.identifier.citation | 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022, pp.1063 - 1070 | - |
dc.identifier.issn | 2375-9232 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312642 | - |
dc.description.abstract | Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i.e., predicting precipitation levels and locations in the near future). Most existing deep-learning-based approaches for precipitation nowcasting, however, consider only radar and/or satellite images as inputs, and meteorological observations collected from ground weather stations, which are sparsely located, are relatively unexplored. In this paper, we propose ASOC, a novel attentive method for effectively exploiting ground-based meteorological observations from multiple weather stations. ASOC is designed to capture temporal dynamics of the observations and also contextual relationships between them. ASOC is easily combined with existing image-based precipitation nowcasting models without changing their architectures. We show that such a combination improves the average critical success index (CSI) of predicting heavy (at least 10 mm/hr) and light (at least 1 mm/hr) rainfall events at 1-6 hr lead times by 5.7%, compared to the original image-based model, using the radar images and ground-based observations around South Korea collected from 2014 to 2020. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data | - |
dc.type | Conference | - |
dc.identifier.wosid | 000971492200130 | - |
dc.identifier.scopusid | 2-s2.0-85148447522 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 1063 | - |
dc.citation.endingpage | 1070 | - |
dc.citation.publicationname | 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Orlando | - |
dc.identifier.doi | 10.1109/ICDMW58026.2022.00138 | - |
dc.contributor.localauthor | Shin, Kijung | - |
dc.contributor.nonIdAuthor | Hwang, Hyunjin | - |
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