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January 1, 2026International Journal of Hydrology Science and Technology

Advancing streamflow prediction with Kolmogorov-Arnold network and LSTM: a comparative analysis

Vipul Santrambhai Varma Jayantilal N. Patel

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Key Points

This research aims to evaluate the effectiveness of Kolmogorov-Arnold network and LSTM in streamflow prediction.
Comparative analysis of Kolmogorov-Arnold network and LSTM models.
Utilization of historical streamflow data for model training and testing.
Focus on prediction accuracy metrics for evaluation.
LSTM outperformed the Kolmogorov-Arnold network in prediction accuracy metrics.
Improved forecasting indicates potential for better water resource management.
Findings suggest significant benefits from using advanced machine learning techniques.

Implication

Comparative analysis of Kolmogorov-Arnold network and LSTM improves streamflow prediction accuracy, suggesting enhanced forecasting methods.

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Cite This Study

Varma et al. (2026) studied this question.

synapsesocial.com/papers/6a0ff327d674f7c03778b9a8 https://doi.org/10.1504/ijhst.2026.10078563

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