Comparison between response surface models and artificial neural networks in hydrologic forecasting

Yu, J., Qin, X., Larsen, O. and Chua, L. 2014, Comparison between response surface models and artificial neural networks in hydrologic forecasting, Journal of hydrologic engineering, vol. 19, no. 3, pp. 473-481, doi: 10.1061/(ASCE)HE.1943-5584.0000827.

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Title Comparison between response surface models and artificial neural networks in hydrologic forecasting
Author(s) Yu, J.
Qin, X.
Larsen, O.
Chua, L.ORCID iD for Chua, L.
Journal name Journal of hydrologic engineering
Volume number 19
Issue number 3
Start page 473
End page 481
Total pages 9
Publisher American Society of Civil Engineers
Place of publication Reston, Va.
Publication date 2014
ISSN 1943-5584
Keyword(s) hydrologic forecasting
response surface model
artificial neural networks
Summary Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN
Language eng
DOI 10.1061/(ASCE)HE.1943-5584.0000827
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2014, American Society of Civil Engineers
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Document type: Journal Article
Collection: School of Engineering
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