Journal
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
Volume 67, Issue 2, Pages 161-174Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2021.2012182
Keywords
support vector machine; simulated annealing integrated with mayfly optimization; streamflow prediction
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This paper proposes a robust accurate streamflow prediction model by balancing the abilities of exploitation and exploration to find the best parameters. By integrating the simulated annealing algorithm and the mayfly optimization algorithm, the proposed method overcomes the exploration weakness of existing methods. The experimental results show that the proposed method outperforms other methods in prediction accuracy.
This paper focuses on the development of a robust accurate streamflow prediction model by balancing the abilities of exploitation and exploration to find the best parameters of a machine learning model. To do so, the simulated annealing (SA) algorithm is integrated with the mayfly optimization algorithm (MOA) as SAMOA to determine the optimal hyper-parameters of support vector regression (SVR) to overcome the exploration weakness of the MOA method. The proposed method is compared with the classical SVR and hybrid SVR-MOA. To examine the accuracy of the selected methods, monthly hydroclimatic data from Jhelum River Basin is used to predict the monthly streamflow on the basis of RMSE, MAE, NSE, and R-2 indices. Test results show that the SVR-SAMOA outperformed the SVR-MOA and SVR models. SVR-SAMOA reduced the prediction errors of the SVR-MOA and SVR models by decreasing the RMSE and the MSE from 21.4% to 14.7% and from 21.7% to 15.1%, respectively, in the test stage.
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