日本地球惑星科学連合2025年大会
セッション概要| 大気水圏科学 (A) | ||||
|---|---|---|---|---|
| セッション小記号 | 計測技術・研究手法 (TT) | |||
| セッション ID | A-TT35 | |||
| タイトル | Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions | |||
| タイトル短縮名 | Machine Learning Techniques applications | |||
| 開催日時 | 口頭セッション | 5/30(金) PM1-PM2 |
現地口頭会場 | 展示場特設会場 (2) [EXH02] |
| 現地 ポスター コアタイム |
5/30(金) PM3 | 現地ポスター会場 | 展示場ホール7・8 | |
| 代表コンビーナ | 氏名 | Jayanthi Venkata Ratnam | ||
| 所属 | Application Laboratory, JAMSTEC | |||
| 共同コンビーナ１ | 氏名 | Patrick Martineau | ||
| 所属 | Japan Agency for Marine-Earth Science and Technology | |||
| 共同コンビーナ２ | 氏名 | 土井 威志 | ||
| 所属 | JAMSTEC | |||
| 共同コンビーナ３ | 氏名 | Behera Swadhin | ||
| 所属 | Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001 | |||
| セッション言語 | E | |||
| スコープ |
Advances in the machine learning techniques such as deep learning have led to an increase in the application of the techniques to a wide range of topics such as weather, climate, ocean, hydrology, and disease predictions. In the recent times, these techniques are being increasingly used to predict extreme events such as malaria outbreaks, heat waves, cold spells, flooding, droughts, tropical cyclones, typhoons, El Nino/Indian Ocean Dipole events among many others. In addition, machine-learning techniques are helping researchers to improve parameterization schemes in numerical prediction models. Machine-learning is also being used to improve numerical model predictions by providing methods to reduce biases and improve the horizontal resolution of the predictions. This session aims to bring together the researchers working on various machine learning techniques to discuss and enhance our understanding of weather, climate, Ocean, hydrology and tropical diseases as well as their predictions and applications for societal benefits and well-being. |
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| 発表方法 | 口頭およびポスターセッション | |||
| 共催情報 | 学協会 | 日本海洋学会 | ||
| ジョイント | - | |||
| 団体会員以外の組織との共催 | - | |||
| 国際連携団体 | - | |||
| 招待講演 |
Philippe Baron（National Institute of Information and Communications Technology） |
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| 時間 | 講演番号 | タイトル | 発表者 |
|---|---|---|---|
| 口頭発表 5月30日 PM1 | |||
| 13:45 - 14:00 | ATT35-01 | Real-time nowcasting of "Guerrilla" rainstorms: Demonstration at Osaka Expo 2025 | Philippe Baron |
| 14:00 - 14:15 | ATT35-02 | 現場採水データを用いた機械学習による海水中二酸化炭素分圧推定モデルの構築 | 林 美鶴 |
| 14:15 - 14:30 | ATT35-03 | Ocean Internal Wave Monitoring System Based on Visible Light Satellite Imagery and Deep Learning Networks | Yu-Lun Lee |
| 14:30 - 14:45 | ATT35-04 | Improving the Numerical Weather Prediction of Daily Maximum Temperature Using Deep Learning Methods | Linna ZHAO |
| 14:45 - 15:00 | ATT35-05 | Artificial Intelligence for Anomaly Detection in Sea Surface Optical Imagery | Olga Bilousova |
| 15:00 - 15:15 | ATT35-06 | Operational forecasting of El Niño-Southern Oscillation (ENSO) Niño3.4 index using ensembles of Convolutional neural networks | Kalpesh Ravindra Patil |
| 口頭発表 5月30日 PM2 | |||
| 15:30 - 15:45 | ATT35-07 | Predicting Characteristics of Salmon Return Migration Using Deep Learning | Mikhail Borisov |
| 15:45 - 16:00 | ATT35-08 | Artificial Neural Networks for Predicting Sea Surface Currents Around the Korean Peninsula | Jae-Hun Park |
| 16:00 - 16:15 | ATT35-09 | Application of Machine Learning Models for Rainfall-Induced Landslide Prediction: Evaluating the Impact of Hydrological Parameters from SWAT | Chih-Mei Lu |
| 16:15 - 16:30 | ATT35-10 | Real-time predictions of the 2023–2025 climate conditions in the tropical Pacific using a purely data-driven Transformer model | Rong-Hua Zhang |
| 16:30 - 16:45 | ATT35-11 | Machine learning-based streamflow estimation using GSMaP_NRT in Marikina River Basin | Nelson Stephen Lising Ventura |
| 16:45 - 17:00 | ATT35-12 | Detection of Irminger Rings in high resolution ocean hydrodynamic modeling data using artificial neural networks | Mikhail Kalinin |
| 講演番号 | タイトル | 発表者 |
|---|---|---|
| ポスター発表 5月30日 PM3 | ||
| ATT35-P01 | A Hierarchical Multi-scale LSTM Model for Improved Water Level Forecasting in the Han River Basin | JONGHO KIM |
| ATT35-P02 | Machine Learning Approach to Reconstruct Vertical T/S Profiles from Satellite-Derived SSH. | Geonmin Lee |
| ATT35-P03 | Spatiotemporal Attention-Enhanced Deep Learning for Improved ENSO Long-Lead-Time Forecasting | Wen-Chieh Wu |
| ATT35-P04 | Skillful prediction of Atlantic Niño index using machine learning models | Jayanthi Venkata Ratnam |
| ATT35-P05 | Machine Learning-Based Bias Correction to Improve Marine Heatwave Forecasts in the Korean Marginal Sea | NaKyoung Im |
| ATT35-P06 | Modeling of turbulent pollutants transport in planetary boundary layer expliting large eddy simulation and machine learning | Ilya Gerasimov |
| ATT35-P07 | Bias correction for numerical weather prediction with artificial neural networks using fine-scale preserving loss | Viktor Artemovich Golikov |