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9956955 
Journal Article 
Prediction of flow duration curves for ungauged basins 
Atieh, M; Taylor, G; Sattar, AMA; Gharabaghi, B 
2017 
Yes 
Journal of Hydrology
ISSN: 0022-1694 
545 
383-394 
WOS:000394399100030 
This study presents novel models for prediction of flow Duration Curves (FDCs) at ungauged basins using artificial neural networks (ANN) and Gene Expression Programming (GEP) trained and tested using historical flow records from 171 unregulated and 89 regulated basins across North America. For the 89 regulated basins, FDCs were generated for both before and after flow regulation. Topographic, climatic, and land use characteristics are used to develop relationships between these basin characteristics and FDC statistical distribution parameters: mean (m) and variance (v). The two main hypotheses that flow regulation has negligible effect on the mean (m) while it the variance (v) were confirmed. The novel GEP model that predicts the mean (GEP-m) performed very well with high R-2 (0.9) and D (0.95) values and low RAE value of 0.25. The simple regression model that predicts the variance (REG-v) was developed as a function of the mean (m) and a flow regulation index (R). The measured performance and uncertainty analysis indicated that the ANN-m was the best performing model with R-2 (0.97), RAE (0.21), D (0.93) and the lowest 95% confidence prediction error interval (+0.22 to +3.49). Both GEP and ANN models were most sensitive to drainage area followed by mean annual precipitation, apportionment entropy disorder index, and shape factor. (C) 2016 Elsevier B.V. All rights reserved. 
Regulated; Ungauged basins; Flow duration curves; Artificial neural networks; Genetic evolutionary program 
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