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

Achievement

Publications
2024
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

  • Invitation of Dr. Ali Ercan to Kumamoto University

  • Visit of Middle East Technical University (September 2-16, 2023)

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


At the hydraulic experiment building of Middle East Technical University

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