[M-GI34] Data-driven approaches for weather and hydrological predictions

2026年5月25日(月) 17:15 〜 19:00 ポスター会場 (幕張メッセ国際展示場 7・8ホール)

In the digital era, data-driven techniques are transforming our understanding and prediction capabilities of complex earth systems. This session aims to explore the cutting-edge methodological and applicational studies for weather, climate and hydrological predictions. Key themes includes: (1) methodological studies to deepen data-driven approaches for geoscience problems, (2) machine/deep learning studies applied for extreme weather-related disasters, (3) climate predictive analysis to discern climate variability, trends, and anomalies, (4) integrating remote sensing and ground data to refine prediction models. This session aims to foster a rich dialogue among experts, highlighting both the advancements and challenges in data-driven environmental modeling. Participants will gain insights into current best practices and envision the future trajectory of this rapidly evolving domain.

+全てマイスケジュールに登録

17:15 〜 19:00

[MGI34-P01] How Similar Is Similar Enough? Evaluating Machine Learning Model Transferability Across Forested Soil Sites with Comparable Properties

*Young gu Her¹, Minhyuk Jeung¹ (1. University of Florida)

17:15 〜 19:00

[MGI34-P02] Data-driven forecasting of wind vectors over the Tohoku region by convolutional LSTM trained on AMeDAS observations with regional NWP inputs

*佐藤圭悟¹、桂木悠作¹、松田安昌¹ (1. 東北大学)

17:15 〜 19:00

[MGI34-P03] Estimating Hydraulic Conductivity of Fractured Rock Masses Using Deep Neural Networks: A Case Study from Taiwan

*Shih-Meng Hsu¹, Cheng-Jun Zheng¹ (1. National Taiwan Ocean University)

17:15 〜 19:00

[MGI34-P04] Seasonal Amplification of Climate Extreme Influence on Inland Water Quality in South Korea

*Young Jun Kim¹, Gibeom Nam¹, Euiho Hwang² (1. Senior Researcher, K-water Satellite Research Center, K-water Research Institute, Daejeon, South Korea, 2. General Director, K-water Satellite Research Center, K-water Research Institute, Daejeon, South Korea)

セッション情報

[M-GI34] Data-driven approaches for weather and hydrological predictions

[MGI34-P01] How Similar Is Similar Enough? Evaluating Machine Learning Model Transferability Across Forested Soil Sites with Comparable Properties

[MGI34-P02] Data-driven forecasting of wind vectors over the Tohoku region by convolutional LSTM trained on AMeDAS observations with regional NWP inputs

[MGI34-P03] Estimating Hydraulic Conductivity of Fractured Rock Masses Using Deep Neural Networks: A Case Study from Taiwan

[MGI34-P04] Seasonal Amplification of Climate Extreme Influence on Inland Water Quality in South Korea

認証