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Design and Performance Verification of Deep Learning-Based River Flood Prediction System Design and Digital Twin-Based Its Application

Author

Listed:
  • Heesang Eom

    (Department of Computer, Graduate School, Seoul Women’s University, Seoul 01797, Republic of Korea)

  • Younghun Kim

    (Department of Computer, Graduate School, Seoul Women’s University, Seoul 01797, Republic of Korea)

  • Jongho Paik

    (Department of Software Convergence, Seoul Women’s University, Seoul 01797, Republic of Korea)

Abstract

This paper presents a digital twin-based river management and flood prediction system designed for hydrological environments, including volcanic geology. To address the problems of rapid runoff and complex terrain, a deep learning-based hybrid model is proposed that integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) units for temporal sequence modeling. The performance evaluation results show that the proposed CNN-RNN hybrid model outperforms individual CNN and RNN baselines. The hybrid model achieves a macro-average precision of 0.97, a recall of 0.99, and an F1 score of 0.98, significantly outperforming existing methods. The system is also integrated with a 3D digital twin visualization platform to enable real-time monitoring and data-driven decision-making.

Suggested Citation

  • Heesang Eom & Younghun Kim & Jongho Paik, 2025. "Design and Performance Verification of Deep Learning-Based River Flood Prediction System Design and Digital Twin-Based Its Application," Mathematics, MDPI, vol. 13(11), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1696-:d:1661460
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