| Project/Area Number |
21K04276
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 22040:Hydroengineering-related
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| Research Institution | Fukuoka Institute of Technology (2023)
Kyushu University (2021-2022) |
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Principal Investigator |
Tai Akira 福岡工業大学, 社会環境学部, 准教授 (20585921)
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| Co-Investigator(Kenkyū-buntansha) |
齋田 倫範 鹿児島大学, 理工学域工学系, 准教授 (80432863)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2023: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2022: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | アンサンブル気候予測データ / 沿岸海洋環境災害 / 気候予測データ / バイアス補正 / 有明海諫早湾 / 潮流 / 溶存酸素濃度 / 河川流量 / 成層度 / 長期観測データ / 機械学習 / 水質予測 / DO濃度 / 密度成層 / 沿岸海洋 / 環境災害 / 気候変動 / d4PDF |
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| Outline of Research at the Start |
本研究では，気候データを入力値とし，密度，溶存酸素などの沿岸海洋において水環境の指標となるデータを出力する水環境予測モデルを機械学習により開発する．開発したモデルを用いて，アンサンブル気候予測データに対して水環境予測を実施することで不確実性を考慮した確率論的環境災害リスク評価を行うことを目的とする．
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| Outline of Final Research Achievements |
In this study, we develop a water environment prediction model using machine learning that takes climate data as input and outputs data indicative of coastal marine water environments, such as density and dissolved oxygen. The developed model is used to perform water environment predictions on ensemble climate prediction data, with the aim of conducting probabilistic environmental disaster risk assessments that consider uncertainties. First, we focused on the changes in the water quality environment at point B3 of the observation tower of the Kyushu Regional Agricultural Administration Office, located in the center of Isahaya Bay, and constructed a support vector regression model. We analyzed long-term observation data in the Ariake Sea and Isahaya Bay to clarify the interannual variations and their relationships of tidal currents, bottom layer dissolved oxygen concentration, river flow, and stratification.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では，気候予測データベースd4PDFに基づく九州地方の降雨及び河川流量の将来変化特性について，有明海・諫早湾の潮汐・潮流の経年変化特性，有明海諫早湾における流動構造の経年変化と水質の関係，河川流量を用いた機械学習による有明海の水質予測などを明らかにし，査読付き論文や学会などで発表を行った．
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