Adnan, Rana Muhammad and Kisi, Ozgur and Mostafa, Reham R. and Ahmed, Ali Najah and Ahmed El-Shafie, Ahmed Hussein Kamel (2022) The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction. Hydrological Sciences Journal, 67 (2). pp. 161-174. ISSN 0262-6667, DOI https://doi.org/10.1080/02626667.2021.2012182.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Support vector machine; Simulated annealing integrated with mayfly optimization; Streamflow prediction |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 15 Aug 2022 01:05 |
| Last Modified: | 15 Aug 2022 01:05 |
| URI: | http://eprints.um.edu.my/id/eprint/33466 |
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