Daily streamflow simulation based on the improved machine learning method

• Main Authors: Kan Guangyuan, He Xiaoyan, Ding Liuqian, Li Jiren, Hong Yang, Ren Minglei, Lei ianjie, Liang Ke, Zuo Depeng, Huang Pengnian
• Format: Article
• Language: Spanish
• Published: Instituto Mexicano de Tecnología del Agua 2017-08-01
• Series: Tecnología y ciencias del agua
• Subjects: Machine learning, daily streamflow simulation, hydrological model, flood forecasting, global optimization
• Online Access: http://www.revistatyca.org.mx/ojs/index.php/tyca/article/view/1306

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.