A new intelligent method for monthly streamflow prediction: hybrid wavelet support vector regression based on grey wolf optimizer (WSVR–GWO)

• Published in: Arabian journal of geosciences Vol. 12; no. 17; pp. 1 - 20
• Main Authors: Tikhamarine, Yazid, Souag-Gamane, Doudja, Kisi, Ozgur
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2019; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Correlation coefficient; Correlation coefficients; Discharge measurement; Drought; Earth and Environmental Science; Earth science; Earth Sciences; Flood management; Forecasting; Gaging stations; Heuristic methods; Hydrologic data; Hydrologic models; Hydrology; Mathematical models; Monthly; Original Paper; Parameters; Particle swarm optimization; Regression; Resource management; Root-mean-square errors; Stream discharge; Stream flow; Support vector machines; Water management; Water resources; Water resources management; Watershed management; Wavelet transforms; Support vector regression; Grey wolf optimization; Streamflow forecasting; Wavelet transform; Evolutionary algorithms
• ISSN: 1866-7511; 1866-7538
• DOI: 10.1007/s12517-019-4697-1

Forecasting daily and monthly streamflows is necessary for short- and long-term water resources management, particularly in extreme cases, e.g., flood and drought. Accurate models are needed to plan and manage water resources in watersheds. Recently, support vector regression (SVR) shows the ability to handle the hydrological forecasting issues, but the accuracy of SVR depends on the appropriate choice of parameters and selection of proper inputs. A new meta-heuristic algorithm called grey wolf optimizer (GWO) is used in the current research in order to improve SVR accuracy in forecasting monthly streamflow. The proposed approach is compared with other evolutionary methods, particle swarm optimization, shuffled complex evolution, and multi-verse optimization, that are employed in tuning SVR parameters. Furthermore, the proposed methods were also combined with wavelet transform and tested using monthly streamflow data from two gauging stations, Ain Bedra and Fermatou, in Algeria. To assess the performance of the developed models, Nash–Sutcliffe efficiency (NSE), correlation coefficient, root mean squared error (RMSE), and mean absolute error (MAE) were used. The obtained results indicate that multi-linear regression provides appropriate input variables to SVR models. According to all of the performance measures used, hybrid models exhibit better performances, in monthly streamflow prediction, compared with single versions. For example, for the Ain Bedra station, the NSEcriterion and the correlation coefficient values increased considerably from 27.36% and 0.5405, for the single models, to 95.72% and 0.9786, for the hybrid models. A great decrease has also been obtained for the RMSE and MAE values, which decreased from 0.1562 m
3
/s and 0.1244 m
3
/s to 0.6433m
3
/s and 0.3047 m
3
/s. In addition, the new GWO algorithm outperformed the other algorithms in terms of both forecasting accuracy and convergence.