Team & Collaborators

The AquaTRAC AI Project is a multidisciplinary and multi-institutional initiative, bringing together expertise in artificial intelligence, hydrology, ecology, and river engineering. The project is led by Dr. Mohamed Saber (Project Principal Investigator) at the Disaster Prevention Research Institute (DPRI), Kyoto University.

This collaborative framework ensures the integration of advanced technologies with domain-specific knowledge to address complex challenges in aquatic ecosystem monitoring.

Kyoto University (Host Institution)

The project is anchored at Kyoto University – Disaster Prevention Research Institute (DPRI), a globally recognized center for research in water resources, disaster risk reduction, and environmental systems.

Core Academic Team

  • Prof. Sameh Kantoush – DPRI, Kyoto University
    Expertise in river engineering, sediment management, and sustainable water systems.
  • Prof. Sohei Kobayashi – DPRI, Kyoto University
    Specialist in hydraulic engineering and river system analysis.
  • Prof. Tetsuya Sumi – DPRI, Kyoto University
    Internationally recognized expert in dam engineering, sedimentation, and reservoir management.
  • Prof. Yasuhiro Takemon – DPRI, Kyoto University
    Leading expert in river ecology and aquatic biodiversity, contributing critical long-term monitoring datasets (including Ayu migration records).

Core Collaborators

The project is further strengthened through international collaboration, expanding its scientific scope and application potential.

  • Prof. Abdel-Rahman Hedar – Assiut University, Egypt
    Expertise in optimization, computational intelligence, and machine learning.
  • Dr. Emad Mabrouk – Assiut University, Egypt
    Specialist in applied mathematics and data-driven modeling.
  • Dr. Tayeb Boulmaiz – University of Annaba, Algeria
    Expertise in environmental systems and hydrological applications.

Collaborative Strength

This diverse team structure enables AquaTRAC AI to:

  • Integrate AI innovation with real-world environmental applications
  • Combine field-based ecological knowledge with advanced computational methods
  • Foster international knowledge exchange and capacity building
  • Support the development of a globally scalable monitoring framework