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Interpolation of Mountain Weather Forecasts by Machine Learning
https://ipsj.ixsq.nii.ac.jp/records/240018
https://ipsj.ixsq.nii.ac.jp/records/240018933f7aec-9b07-4c8f-a98e-ce64195397be
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-JNL6510010.pdf (7.3 MB)
2026年10月15日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥0, IPSJ:学会員：¥0, 論文誌:会員：¥0, DLIB:会員：¥0 | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2024-10-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Interpolation of Mountain Weather Forecasts by Machine Learning | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Interpolation of Mountain Weather Forecasts by Machine Learning | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [一般論文] weather forecast, machine learning | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology | ||||||||||
| 著者所属 | ||||||||||
| Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology | ||||||||||
| 著者名 |
Kazuma, Iwase
× Kazuma, Iwase
× Tomoyuki, Takenawa
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| 著者名(英) |
Kazuma, Iwase
× Kazuma, Iwase
× Tomoyuki, Takenawa
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Recent advances in numerical simulation methods based on physical models and their combination with machine learning have improved the accuracy of weather forecasts. However, the accuracy decreases in complex terrains such as mountainous regions because these methods usually use grids of several kilometers square and simple machine learning models. While deep learning has also made significant progress in recent years, its direct application is difficult to utilize the physical knowledge used in the simulation. This paper proposes a method that uses machine learning to interpolate future weather in mountainous regions using forecast data from surrounding plains and past observed data to improve weather forecasts in mountainous regions. We focus on mountainous regions in Japan and predict temperature and precipitation mainly using LightGBM as a machine learning model. Despite the use of a small dataset, through feature engineering and model tuning, our method partially achieves improvements in the RMSE with significantly less training time. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.873 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Recent advances in numerical simulation methods based on physical models and their combination with machine learning have improved the accuracy of weather forecasts. However, the accuracy decreases in complex terrains such as mountainous regions because these methods usually use grids of several kilometers square and simple machine learning models. While deep learning has also made significant progress in recent years, its direct application is difficult to utilize the physical knowledge used in the simulation. This paper proposes a method that uses machine learning to interpolate future weather in mountainous regions using forecast data from surrounding plains and past observed data to improve weather forecasts in mountainous regions. We focus on mountainous regions in Japan and predict temperature and precipitation mainly using LightGBM as a machine learning model. Despite the use of a small dataset, through feature engineering and model tuning, our method partially achieves improvements in the RMSE with significantly less training time. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.873 ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 65, 号 10, 発行日 2024-10-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
| 公開者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
