RESEARCH

Research Clusters

Deep Learning for Hydrology

Research Keyword: Deep Learning, Hydrology

The primary goals of this research consist of three key aspects:

 1. employing advanced deep learning approaches to address hydrological problems,

 2. evaluating the suitability of such deep learning techniques, and

 3. simultaneously developing new deep learning architectures specifically designed for hydrological challenges.

Deep Learning for Hydrology

Cluster members

  • Japan
    Coordinator
    Kei ISHIDA
    Associate Professor, Center for Water Cycle, Marine Environment, and Disaster Management, Kumamoto University
    Japan
  • Japan
    Motoki AMAGASAKI
    Professor, Faculty of Advanced Science and Technology, Kumamoto University
    Japan
  • Japan
    Masato KIYAMA
    Assistant Professor, Faculty of Advanced Science and Technology, Kumamoto University
    Japan
  • Turkey
    Ali ERCAN
    Associate Professor, Civil Engineering Department, Middle East Technical University
    Turkey
  • China
    Tongbi TU
    Associate Professor, School of Civil Engineering, Sun Yat-Sen University
    China

Achievements

Publications
  1. Izumi, T., Amagasaki, M., Ishida, K., Kiyama, M., 2022. Super-resolution of sea surface temperature with convolutional neural network- and generative adversarial network-based methods. J. Water Clim. Chang. 13, 1673–1683.
  2. 永里 赳義, 石田 桂, 坂口 大珠: Out-of-Sample LSTM による高解像度積雪深分布推定, AIデータサイエンス論文集, Vol3, No.J2, p889-897, 2022.
  3. 坂口 大珠, 石田 桂, 永里 赳義: リサンプリングとアンサンブル学習を用いた深層学習降雨流出モデルの精度向上の試み, AIデータサイエンス論文集, Vol3, No.J2, p906-945, 2022.
Grants

Activities

・Visit of Middle East Technical University (March 13-22, 2023)


At the hydraulic experiment building of Middle East Technical University

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