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The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction

Balancing; Forecasting; Stream flow; Support vector machines; Exploitation and explorations; Machine learning models; Optimisations; Optimization algorithms; Prediction modelling; Simulated annealing integrated with mayfly optimization; Streamflow prediction; Support vector regression models; Suppor...

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Main Authors: Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A.
Other Authors: 56689592000
Format: Article
Published: Taylor and Francis Ltd. 2023
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spelling my.uniten.dspace-272712023-05-29T17:41:55Z The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction Adnan R.M. Kisi O. Mostafa R.R. Ahmed A.N. El-Shafie A. 56689592000 6507051085 57216628949 57214837520 16068189400 Balancing; Forecasting; Stream flow; Support vector machines; Exploitation and explorations; Machine learning models; Optimisations; Optimization algorithms; Prediction modelling; Simulated annealing integrated with mayfly optimization; Streamflow prediction; Support vector regression models; Support vector regressions; Support vectors machine; Simulated annealing; algorithm; mayfly; optimization; prediction; streamflow; support vector machine; Jhelum River 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 R2 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. � 2022 IAHS. Final 2023-05-29T09:41:55Z 2023-05-29T09:41:55Z 2022 Article 10.1080/02626667.2021.2012182 2-s2.0-85123408754 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123408754&doi=10.1080%2f02626667.2021.2012182&partnerID=40&md5=6dfa661d63bd74946ae878224168fff8 https://irepository.uniten.edu.my/handle/123456789/27271 67 2 161 174 Taylor and Francis Ltd. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Balancing; Forecasting; Stream flow; Support vector machines; Exploitation and explorations; Machine learning models; Optimisations; Optimization algorithms; Prediction modelling; Simulated annealing integrated with mayfly optimization; Streamflow prediction; Support vector regression models; Support vector regressions; Support vectors machine; Simulated annealing; algorithm; mayfly; optimization; prediction; streamflow; support vector machine; Jhelum River
author2 56689592000
author_facet 56689592000
Adnan R.M.
Kisi O.
Mostafa R.R.
Ahmed A.N.
El-Shafie A.
format Article
author Adnan R.M.
Kisi O.
Mostafa R.R.
Ahmed A.N.
El-Shafie A.
spellingShingle Adnan R.M.
Kisi O.
Mostafa R.R.
Ahmed A.N.
El-Shafie A.
The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
author_sort Adnan R.M.
title The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
title_short The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
title_full The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
title_fullStr The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
title_full_unstemmed The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
title_sort potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
publisher Taylor and Francis Ltd.
publishDate 2023
_version_ 1806423356757508096
score 13.222552

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