Daily streamflow simulation based on the improved machine learning method

Authors

  • Kan Guangyuan China Institute of Water Resources and Hydropower Research, China/Tsinghua University, China
  • He Xiaoyan China Institute of Water Resources and Hydropower Research, China
  • Ding Liuqian China Institute of Water Resources and Hydropower Research, China
  • Li Jiren China Institute of Water Resources and Hydropower Research, China
  • Hong Yang Tsinghua University, China/University of Oklahoma, USA
  • Ren Minglei China Institute of Water Resources and Hydropower Research, China
  • Lei ianjie China Institute of Water Resources and Hydropower Research, China
  • Liang Ke Hohai University, China
  • Zuo Depeng Beijing Normal University, China
  • Huang Pengnian Nanjing University of Information Sciences & Technology, China

DOI:

https://doi.org/10.24850/j-tyca-2017-02-05

Keywords:

Machine learning, daily streamflow simulation, hydrological model, flood forecasting, global optimization

Abstract

Kan, G., He, X., Ding, L., Li, J., Hong, Y., Ren, M., Lei, T., Liang, K., Zuo, D., & Huang, P. (March-April, 2017). Daily streamflow simulation based on the improved machine learning method. Water Technology and Sciences (in Spanish), 8(2), 51-60.

Daily streamflow simulation has usually been implemented by conceptual or distributed hydrological models. Nowadays, hydrological data, which can be easily obtained from automatic measuring systems, are more than enough. Therefore, machine learning turns into an effective and popular tool which is highly suited for the streamflow simulation task. In this paper, we propose an improved machine learning method referred to as PKEK model based on the previously proposed NU-PEK model for the purpose of generating daily streamflow simulation results with better accuracy and stability. Comparison results between the PKEK model and the NU-PEK model indicated that the improved model has better accuracy and stability and has a bright application prospect for daily streamflow simulation tasks.

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How to Cite

Guangyuan, K., Xiaoyan, H., Liuqian, D., Jiren, L., Yang, H., Minglei, R., … Pengnian, H. (2017). Daily streamflow simulation based on the improved machine learning method. Tecnología Y Ciencias Del Agua, 8(2), 51–80. https://doi.org/10.24850/j-tyca-2017-02-05

Issue

Vol. 8 No. 2 (2017)

Section

Articles

License

By Instituto Mexicano de Tecnología del Agua is distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Based on a work at https://www.revistatyca.org.mx/. Permissions beyond what is covered by this license can be found in Editorial Policy.