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Advances in Meteorology
Volume 2017 (2017), Article ID 2391621, 23 pages
Research Article

Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

1School of Hydropower and Information Engineering, Huazhong University of Science & Technology, Wuhan 430074, China
2Center for Interdisciplinary Research, International Black Sea University, Tbilisi, Georgia
3Faculty of Agricultural Engineering & Technology, Department of Farm Machinery & Power, University of Agriculture, Faisalabad, Pakistan

Correspondence should be addressed to Xiaohui Yuan; nc.ude.tsuh@17hxy

Received 1 December 2016; Revised 15 December 2016; Accepted 18 December 2016; Published 19 January 2017

Academic Editor: James Cleverly

Copyright © 2017 Rana Muhammad Adnan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.