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Water 2019, 11(1), 85; https://doi.org/10.3390/w11010085

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
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Received: 23 November 2018 / Revised: 29 December 2018 / Accepted: 1 January 2019 / Published: 6 January 2019
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Abstract

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 streamflow; artificial neural network; simulation; forecasting; support vector machine; evolutionary strategy
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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.

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