A Study on Machine Learning -based River Water Level Prediction Model for Flood Prevention 


Vol. 14,  No. 6, pp. 431-450, Jun.  2025
https://doi.org/10.3745/TKIPS.2025.14.6.431


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  Abstract

This study compares various data-driven modeling approaches for river water level prediction to support the development of accurate forecasting systems for disaster response. Initially, a CNN-based model was applied for image-based classification of water level stages. Although the model achieved high accuracy, limitations in generalization were observed due to class imbalance and location-specific bias. Techniques such as dropout and data augmentation were introduced, but they offered limited improvement. Subsequent time series prediction experiments employed linear regression, polynomial regression, and LSTM models, comparing Shift and Sliding Window input methods. Across all models, the Sliding Window method consistently outperformed the Shift method in terms of R², NSE, and PBIAS, with especially notable differences in long-term forecasts. The multivariate LSTM model, which incorporated additional variables such as flow rate, demonstrated superior accuracy and stability compared to its univariate counterpart, achieving R² values exceeding 0.96 for 3-hour forecasts. Overall, the combination of the Sliding Window approach and multivariate modeling was shown to be highly effective for improving prediction performance. Future work will explore advanced nonlinear models such as Transformers, as well as multimodal data integration including rainfall and CCTV imagery to enhance the accuracy and applicability of river level forecasting systems.

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[IEEE Style]

D. You, H. Roh, S. Lim, S. Baik, Y. Hong, "A Study on Machine Learning -based River Water Level Prediction Model for Flood Prevention," The Transactions of the Korea Information Processing Society, vol. 14, no. 6, pp. 431-450, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.6.431.

[ACM Style]

DeokHyun You, Heesun Roh, Suyeon Lim, Seongbok Baik, and Yong-Geun Hong. 2025. A Study on Machine Learning -based River Water Level Prediction Model for Flood Prevention. The Transactions of the Korea Information Processing Society, 14, 6, (2025), 431-450. DOI: https://doi.org/10.3745/TKIPS.2025.14.6.431.

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