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Open AccessArticle

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

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Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Bahman Boulevard 29, Iran
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Department of Hydrosciences, Technische Univeristät Dresden, 01069 Dresden, Germany
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Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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School of the Built Environment, Oxford Brookes University, Oxford OX30BP, UK
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Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
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Queensland University of Technology, Brisbane QLD 4059, Australia
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chí Minh, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chí Minh, Vietnam
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School of Architecture, Design and the Built Environment, Nottingham Trent University, 50 Shakespeare St, Nottingham NG1 4FQ, UK
*
Author to whom correspondence should be addressed.
Water 2019, 11(9), 1934; https://doi.org/10.3390/w11091934
Received: 20 June 2019 / Revised: 10 July 2019 / Accepted: 11 July 2019 / Published: 17 September 2019
(This article belongs to the Section Hydrology and Hydrogeology)
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow prediction. This paper aims to identify models with higher accuracy, robustness, and generalization ability by inspecting the accuracy of a number of machine learning models. The proposed models for river flow include support vector regression (SVR), a hybrid of SVR with a fruit fly optimization algorithm (FOA) (so-called FOASVR), and an M5 model tree (M5). Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of the proposed models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performance in forecasting river flows at Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Qt−1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both the FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of the FOASVR was moderately better than the M5 and periodicity noticeably increased the performance of the models; consequently, FOASVR can be suggested as the most accurate method for forecasting river flows. View Full-Text
Keywords: river flow forecasting; stream flow; hybrid machine learning; M5 model tree; fruit fly optimization algorithm (FOA); support vector regression; big data; deep learning; hydro-informatics river flow forecasting; stream flow; hybrid machine learning; M5 model tree; fruit fly optimization algorithm (FOA); support vector regression; big data; deep learning; hydro-informatics
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MDPI and ACS Style

Samadianfard, S.; Jarhan, S.; Salwana, E.; Mosavi, A.; Shamshirband, S.; Akib, S. Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin. Water 2019, 11, 1934.

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