Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79536
Title: Comparison between response surface models and artificial neural networks in hydrologic forecasting
Authors: Yu, Jianjun
Qin, Xiaosheng
Larsen, Ole
Chua, Lloyd Hock Chye
Keywords: DRNTU::Engineering::Civil engineering::Water resources
Issue Date: 2014
Source: Yu, J., Qin, X., Larsen, O., & Chua, L. H. C. (2014). Comparison between Response Surface Models and Artificial Neural Networks in Hydrologic Forecasting. Journal of Hydrologic Engineering, 19(3), 473-481.
Series/Report no.: Journal of hydrologic engineering
Abstract: 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.
URI: https://hdl.handle.net/10356/79536
http://hdl.handle.net/10220/19646
ISSN: 1084-0699
DOI: 10.1061/(ASCE)HE.1943-5584.0000827
Rights: © 2014 American Society of Civil Engineers. This is the author created version of a work that has been peer reviewed and accepted for publication by Journal of Hydrologic Engineering, American Society of Civil Engineers. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI:http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000827].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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