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

journal contribution
posted on 01.01.2014, 00:00 authored by J Yu, X Qin, O Larsen, Lloyd ChuaLloyd Chua
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

History

Journal

Journal of hydrologic engineering

Volume

19

Issue

3

Pagination

473 - 481

Publisher

American Society of Civil Engineers

Location

Reston, Va.

ISSN

1943-5584

Language

eng

Notes

MARCH

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2014, American Society of Civil Engineers