Atmospheric and Hydrospheric Sciences (A) | ||
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Session Sub-category | Technology &Techniques(TT) | |
Session ID | A-TT35 | |
Title | Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions | |
Short Title | Machine Learning Techniques applications | |
Main Convener | Name | Venkata Ratnam Jayanthi |
Affiliation | Application Laboratory, JAMSTEC | |
Co-Convener 1 | Name | Patrick Martineau |
Affiliation | Japan Agency for Marine-Earth Science and Technology | |
Co-Convener 2 | Name | Takeshi Doi |
Affiliation | JAMSTEC | |
Co-Convener 3 | Name | Swadhin Behera |
Affiliation | Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001 | |
Session Language |
E |
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Scope |
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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Presentation Format | Oral and Poster presentation | |
Invited Authors |
Philippe Baron (National Institute of Information and Communications Technology) |
Time | Presentation No | Title | Presenter |
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Oral Presentation May 30 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 | Construction of a estimation model for the partial pressure of carbon dioxide in seawater by machine learning using on-site water sampling data. | Mitsuru Hayashi |
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 |
Oral Presentation May 30 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 |
Presentation No | Title | Presenter |
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Poster Presentation May 30 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 | Venkata Ratnam Jayanthi |
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 |