Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(1), 85; https://doi.org/10.3390/w11010085
Received: 23 November 2018 / Revised: 29 December 2018 / Accepted: 1 January 2019 / Published: 6 January 2019
(This article belongs to the Special Issue Statistical Analysis and Stochastic Modelling of Hydrological Extremes)
The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization and climate change, which contribute to an increase in runoff. This study applied ɛ-Support Vector Machine (ε-SVM) and artificial neural network (ANN) for the simulation and forecasting streamflow of three different catchments. The Evolutionary Strategy (ES) optimization method was used to optimize the ANN and SVM sensitive parameters. The relative performance of the two models was compared, and the results indicate that both models performed well for humid and semi-humid systems, and SVM generally perform better than ANN in the streamflow simulation of all catchments.
View Full-Text
Keywords:
streamflow; artificial neural network; simulation; forecasting; support vector machine; evolutionary strategy
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Bafitlhile, T.M.; Li, Z. Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China. Water 2019, 11, 85. https://doi.org/10.3390/w11010085
AMA Style
Bafitlhile TM, Li Z. Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China. Water. 2019; 11(1):85. https://doi.org/10.3390/w11010085
Chicago/Turabian StyleBafitlhile, Thabo M.; Li, Zhijia. 2019. "Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China" Water 11, no. 1: 85. https://doi.org/10.3390/w11010085
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Search more from Scilit
