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 和文 Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions
英文 Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions
タイトル短縮名 和文 Machine Learning Techniques applications
英文 Machine Learning Techniques applications
代表コンビーナ 氏名 和文 Jayanthi Venkata Ratnam
英文 Venkata Ratnam Jayanthi
所属 和文 Application Laboratory, JAMSTEC
英文 Application Laboratory, JAMSTEC
共同コンビーナ 1 氏名 和文 Patrick Martineau
英文 Patrick Martineau
所属 和文 Japan Agency for Marine-Earth Science and Technology
英文 Japan Agency for Marine-Earth Science and Technology
共同コンビーナ 2 氏名 和文 土井 威志
英文 Takeshi Doi
所属 和文 JAMSTEC
英文 JAMSTEC
共同コンビーナ 3 氏名 和文 Behera Swadhin
英文 Swadhin Behera
所属 和文 Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001
英文 Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001
発表言語 E
スコープ 和文 
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.
英文 
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.
発表方法 口頭および（または）ポスターセッション
招待講演 Philippe Baron (National Institute of Information and Communications Technology)
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