{"created":"2025-01-19T01:43:57.638289+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240018","sets":["581:11492:11503"]},"path":["11503"],"owner":"44499","recid":"240018","title":["Interpolation of Mountain Weather Forecasts by Machine Learning"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-10-15"},"_buckets":{"deposit":"033b7294-4b8e-4dc1-9045-53f9d4fafa68"},"_deposit":{"id":"240018","pid":{"type":"depid","value":"240018","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Interpolation of Mountain Weather Forecasts by Machine Learning","author_link":["658044","658046","658047","658045"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Interpolation of Mountain Weather Forecasts by Machine Learning"},{"subitem_title":"Interpolation of Mountain Weather Forecasts by Machine Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[一般論文] weather forecast, machine learning","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2024-10-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology"},{"subitem_text_value":"Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Marine Science and Technology, Tokyo University of Marine Science and Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/240018/files/IPSJ-JNL6510010.pdf","label":"IPSJ-JNL6510010.pdf"},"date":[{"dateType":"Available","dateValue":"2026-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6510010.pdf","filesize":[{"value":"7.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"109b9232-94c7-4330-8909-7894a693a609","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kazuma, Iwase"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoyuki, Takenawa"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kazuma, Iwase","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoyuki, Takenawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"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.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.32(2024) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.32.873\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"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.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.32(2024) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.32.873\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2024-10-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"65"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":240018,"updated":"2025-01-19T08:06:51.735865+00:00","links":{}}