Daily streamflow simulation based on the improved machine learning method/Simulación de caudales diarios mediante el método de aprendizaje automático mejorado

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 h...

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Bibliographic Details
Published inTecnología y Ciencias del Agua Vol. 8; no. 2; p. 51
Main Authors Kan, Guangyuan, He, Xiaoyan, Ding, Liuqian, Li, Jiren, Hong, Yang, Ren, Minglei, Lei, Tianjie, Liang, Ke, Zuo, Depeng, Huang, Pengnian
Format Journal Article
LanguageEnglish
Published Jiutepec Instituto Mexicano de Tecnologia de Agua 01.03.2017
Subjects
Accuracy
Automation
Aversion learning
Cluster analysis
Computer simulation
Daily
Data processing
Hydrologic cycle
Hydrologic data
Hydrologic models
Hydrology
Learning algorithms
Machine learning
Neural networks
Serial learning
Simulation
Stability
Stream discharge
Stream flow
System effectiveness
Beijing China
United States > US
China
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Summary: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.
ISSN:0187-8336
2007-2422