Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

Sajjad Abdollahi (), Jalil Raeisi, Mohammadreza Khalilianpour, Farshad Ahmadi and Ozgur Kisi
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Sajjad Abdollahi: Shahid Chamran University
Jalil Raeisi: Shahrekord University
Mohammadreza Khalilianpour: Shahrekord University
Farshad Ahmadi: Shahid Chamran University
Ozgur Kisi: Ilia State University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 15, No 9, 4855-4874

Abstract: Abstract This study examines and compares the performance of four new attractive artificial intelligence techniques including artificial neural network (ANN), hybrid wavelet-artificial neural network (WANN), Genetic expression programming (GEP), and hybrid wavelet-genetic expression programming (WGEP) for daily mean streamflow prediction of perennial and non-perennial rivers located in semi-arid region of Zagros mountains in Iran. For this purpose, data of daily mean streamflow of the Behesht-Abad (perennial) and Joneghan (non-perennial) rivers as well as precipitation information of 17 meteorological stations for the period 1999–2008 were used. Coefficient of determination (R2) and root mean square error (RMSE) were used for evaluating the applicability of developed models. This study showed that although the GEP model was the most accurate in predicting peak flows, but in overall among the four mentioned models in both perennial and non-perennial rivers, WANN had the best performance. Among input patterns, flow based and coupled precipitation-flow based patterns with negligible difference to each other were determined to be the best patterns. Also this study confirmed that combining wavelet method with ANN and GEP and developing WANN and WGEP methods results in improving the performance of ANN and GEP models.

Keywords: Artificial neural networks; Genetic expressing programming perennial and non-perennial rivers; Streamflow prediction; Wavelet analysis (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11269-017-1782-7

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