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http://hdl.handle.net/10397/88572
Title: | Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling | Authors: | Wang, Z Attar, NF Khalili, K Behmanesh, J Band, SS Mosavi, A Chau, KW |
Issue Date: | 1-Jan-2020 | Source: | Engineering applications of computational fluid mechanics, 1 Jan. 2020, , v. 14, no. 1, p. 1351-1372 | Abstract: | Accurate streamflow prediction is essential in reservoir management, flood control, and operation of irrigation networks. In this study, the deterministic and stochastic components of modeling are considered simultaneously. Two nonlinear time series models are developed based on autoregressive conditional heteroscedasticity and self-exciting threshold autoregressive methods integrated with the gene expression programming. The data of four stations from four different rivers from 1971 to 2010 are investigated. For examining the reliability and accuracy of the proposed hybrid models, three evaluation criteria, namely the R-2, RMSE, and MAE, and several visual plots were used. Performance comparison of the hybrid models revealed that the accuracy of the SETAR-type models in terms of R-2 performed better than the ARCH-type models for Daryan (0.99), Germezigol (0.99), Ligvan (0.97), and Saeedabad (0.98) at the validation stage. Overall, prediction results showed that a combination of the SETAR with the GEP model performs better than ARCH-based GEP models for the prediction of the monthly streamflow. | Keywords: | Integrated hybrid models Nonlinear time series models Streamflow modeling Gene expression programming Urmia lake basin Stochastic and deterministic |
Publisher: | Hong Kong Polytechnic University, Department of Civil and Structural Engineering | Journal: | Engineering applications of computational fluid mechanics | ISSN: | 1994-2060 | EISSN: | 1997-003X | DOI: | 10.1080/19942060.2020.1830858 | Rights: | © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The following publication Zhen Wang , Nasrin Fathollahzadeh Attar , Keivan Khalili , Javad Behmanesh ,Shahab S. Band , Amir Mosavi & Kwok-wing Chau (2020) Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling, Engineering Applications of Computational Fluid Mechanics, 14:1, 1351-1372 is available at https://dx.doi.org/10.1080/19942060.2020.1830858 |
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