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Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea

Abstract

It is no doubt that the reliable runoff simulation for proper water resources management is essential. In the past, the runoff was generally modeled from hydrologic models that analyze the rainfall-runoff relationship of the basin. However, since techniques have developed rapidly, it has been attempted to apply especially deep-learning technique for hydrological studies as an alternative to the hydrologic model. The objective of the study is to examine whether the deep-learning technique can completely replace the hydrologic model and show how to improve the performance of runoff simulation using deep-learning technique. The runoff in the Hyeongsan River basin, South Korea from 2013 to 2020 were simulated using two models, (1) long short-term memory model that is a deep learning technique widely used in the hydrological study and (2) TANK model, and then we compared the runoff modeling results from both models. The results suggested that it is hard to completely replace the hydrological model with the deep-learning technique due to its simulating behavior and discussed how to improve the reliability of runoff simulation results. Also, a method to improve the efficiency of runoff simulation through a hybrid model which is a combination of two approaches, deep-learning technique and hydrologic model was presented.

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Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2017R1A2B3005695).

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B3005695).

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All authors contributed to the study conception and design. Conceptualization: [JK], [HH], [HSK]; Methodology: [JK], [HH]; Data collection: [JK], [SK], Formal analysis and investigation: [HH], [SK]; Writing—original draft preparation: [JK], [HH], [SK]; Writing—review and editing: [JK], [HSK]; Supervision: [JK], [HSK]; All authors read and approved the final manuscript.

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Correspondence to Heechan Han.

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Kwak, J., Han, H., Kim, S. et al. Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea. Stoch Environ Res Risk Assess (2021). https://doi.org/10.1007/s00477-021-02094-x

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Keywords

  • Deep-learning
  • LSTM model
  • Runoff simulation
  • TANK model