The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction. Issue 2 (25th January 2022)

• Record Type: Journal Article
• Authors: Adnan, Rana Muhammad; Kisi, Ozgur; Mostafa, Reham R.; Ahmed, Ali Najah; El-Shafie, Ahmed
• Is Part Of: Hydrological sciences journal. Volume 67:Issue 2(2022)
• Page Start: 161
• Page End: 174
• Publication Date: 2022-01-25
• Subjects: support vector machine -- simulated annealing integrated with mayfly optimization -- streamflow prediction; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2021.2012182
• Languages: English
• ISSNs: 0262-6667

Abstract

ABSTRACT: 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.