Daily streamflow simulation based on the improved machine learning method
| Autor: | Kan Guangyuan, He Xiaoyan, Ding Liuqian, Li Jiren, Hong Yang, Ren Minglei, Lei ianjie, Liang Ke, Zuo Depeng, Huang Pengnian |
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| Jazyk: | English<br />Spanish; Castilian |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Tecnología y ciencias del agua, Vol 8, Iss 2, Pp 51-80 (2017) |
| Druh dokumentu: | article |
| ISSN: | 0187-8336 2007-2422 |
| DOI: | 10.24850/j-tyca-2017-02-05 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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