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Comparison between response surface models and artificial neural networks in hydrologic forecasting
journal contribution
posted on 2014-01-01, 00:00 authored by J Yu, X Qin, O Larsen, Lloyd ChuaLloyd ChuaDeveloping 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 engineeringVolume
19Issue
3Pagination
473 - 481Publisher
American Society of Civil EngineersLocation
Reston, Va.Publisher DOI
ISSN
1943-5584Language
engNotes
MARCHPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2014, American Society of Civil EngineersUsage metrics
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hydrologic forecastingregressionresponse surface modelartificial neural networksScience & TechnologyTechnologyLife Sciences & BiomedicinePhysical SciencesEngineering, CivilEnvironmental SciencesWater ResourcesEngineeringEnvironmental Sciences & EcologyHydrologyForecastingNeural networksFloodsTIME-SERIESPREDICTIONRUNOFFRAINFALLSUITABILITYCHAOS
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