| 研究課題/領域番号 |
18KK0117
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| 研究種目 |
国際共同研究加速基金(国際共同研究強化(B))
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| 配分区分 | 基金 |
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| 審査区分 |
中区分22:土木工学およびその関連分野
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| 研究機関 | 東京大学 |
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研究代表者 |
金 炯俊 東京大学, 生産技術研究所, 特任准教授 (70635218)
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| 研究分担者 |
渡部 哲史 九州大学, 比較社会文化研究院, 准教授 (20633845)
内海 信幸 東京工業大学, 環境・社会理工学院, 准教授 (60594752)
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| 研究期間 (年度) |
2018-10-09 – 2024-03-31
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| 研究課題ステータス |
完了 (2023年度)
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| 配分額 *注記 |
17,810千円 (直接経費: 13,700千円、間接経費: 4,110千円)
2022年度: 4,290千円 (直接経費: 3,300千円、間接経費: 990千円)
2021年度: 3,510千円 (直接経費: 2,700千円、間接経費: 810千円)
2020年度: 3,510千円 (直接経費: 2,700千円、間接経費: 810千円)
2019年度: 3,510千円 (直接経費: 2,700千円、間接経費: 810千円)
2018年度: 2,990千円 (直接経費: 2,300千円、間接経費: 690千円)
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| キーワード | Physics-informed AI / TWS / Data-driven Forecast / Satellite remote sensing / GPM / Multi-task learning / Precipitation / lake surface area / remote sensing / water big data / データ駆動型モデリング / 水文季節予報 / 衛星観測 / テレコネクション / 人工知能 / 衛星高度計 / 河川の水位 / 海水面温度 / 陸域水貯留 / ニューラルネットワーク / 長期リードタイム予測 / data-driven modeling / seasonal prediction / satellite remote sensing |
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| 研究実績の概要 |
This international collaborative project aimed to develop a data-driven inference framework to predict flood and drought events at lead times ranging up to 6 months to the present. The project involved a partnership between the University of Tokyo (U-Tokyo) in Japan and the National Aeronautics and Space Administration (NASA) in the United States. The NASA team contributed in-depth knowledge and expertise on satellite observations, specifically elucidating the memory impact of local water storage, such as terrestrial water storage and river water height as a delayed local response. The U-Tokyo team focused on elucidating the teleconnection mechanisms between global-scale forcings. They simulated these global and local lagged relations using a physics-informed deep learning approach developed by the U-Tokyo team. Newly proposed deep learning approach proved effective in simulating the complex relationships between global forcings and local hydrology. During the project period, the team faced two unexpected and severe situations: 1) the COVID-19 pandemic, which disrupted international collaboration, and 2) the relocation of the international counterpart from NASA to the University of Saskatchewan, which required adjustments in communication and coordination. Nenvertheless, the project successfully developed a data-driven framework to predict flood and drought. The framework leverages satellite observations, advanced modeling techniques, and international collaboration to provide valuable insights for disaster risk management and water resource planning.
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