2023 Fiscal Year Annual Research Report
物理プロセスを組み込んだ深層学習による水田内温度環境の予測モデルの構築
| Project/Area Number |
22KJ0781
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| Allocation Type | Multi-year Fund |
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| Research Institution | The University of Tokyo |
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Principal Investigator |
謝 文鵬 東京大学, 生産技術研究所, 特別研究員(PD)
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Keywords | paddy field / PGNN / water temperature |
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| Outline of Annual Research Achievements |
Firstly, a paper titled "Interpretable Framework of Physics-guided Neural Network with Attention Mechanism: Simulating Paddy Field Water Temperature Variations" was published in the esteemed journal WRR. This work elucidates the interpretability of a novel neural network framework integrated with physics guidance and attention mechanisms, specifically applied to simulate variations in paddy field water temperature.
Furthermore, another paper was published in 2022 in the journal Irrigation and Drainage, titled "Development of a short-term hybrid forecast model of paddy water temperature as an alert system for high-temperature damage." This research focuses on the development of a hybrid forecasting model tailored for predicting short-term paddy water temperature variations, crucial for mitigating high-temperature damage in agricultural settings.
Additionally, active participation was observed in the AGU conferences of 2022 and 2023, where presentations were delivered to showcase the latest advancements in the research domain.
In summary, over the course of these two years, significant insights have been gained into the integration of deep learning and physical models, along with an understanding of the advantages that deep learning offers for modeling natural phenomena. This research has notably enhanced the understanding and prediction capabilities of dynamic variations in paddy field temperatures. Moreover, valuable insights have been derived for informing agricultural irrigation systems, policy support, and guiding recommendations aimed at optimizing agricultural yields.
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