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Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization

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dc.citedby73
dc.contributor.authorTikhamarine Y.en_US
dc.contributor.authorSouag-Gamane D.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57210575507en_US
dc.contributor.authorid55363629300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:07:25Z
dc.date.available2023-05-29T08:07:25Z
dc.date.issued2020
dc.descriptionAutocorrelation; Biomimetics; Forecasting; Multilayer neural networks; Particle swarm optimization (PSO); Rain; Runoff; Support vector machines; Swarm intelligence; Autocorrelation functions; Cross-correlation function; Hyper-parameter optimizations; Least squares support vector machines; Machine learning methods; Partial autocorrelation function; Rainfall - Runoff modelling; Rainfall-runoff relationship; Learning systems; accuracy assessment; autocorrelation; hydrological cycle; machine learning; numerical method; optimization; rainfall-runoff modeling; Parabuteoen_US
dc.description.abstractRainfall and runoff are considered the main components in the hydrological cycle. Developing an accurate model to capture the dynamic connection between rainfall and runoff remains a problematic task for engineers. Several studies have been carried out to develop models to accurately predict the changes in runoff from rainfall. However, these models have limitations in terms of accuracy and complexity when large numbers of parameters are needed. Therefore, recently, with the advancement of data-driven techniques, a vast number of hydrologists have adopted models to predict changes in runoff. However, data-driven models still encounter several limitations related to hyperparameter optimization and overfitting. Hence, there is a need to improve these models to overcome these limitations. In this study, data-driven techniques such as a Multi-Layer Perceptron (MLP) neural network and Least Squares Support Vector Machine (LSSVM) are integrated with an advanced nature-inspired optimizer, namely, Harris Hawks Optimization (HHO) to model the rainfall-runoff relationship. Five different scenarios will be examined based on the autocorrelation function (ACF), cross-correlation function (CCF) and partial autocorrelation function (PACF). Finally, for comprehensive analysis, the performance of the proposed model will then be compared with integrated data-driven techniques with particle swarm optimization (PSO). The results revealed that all the augmented models with HHO outperformed other integrated models with PSO in predicting the changes in runoff. In addition, a high level of accuracy in predicting runoff values was achieved when HHO was integrated with LSSVM. � 2020 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo125133
dc.identifier.doi10.1016/j.jhydrol.2020.125133
dc.identifier.scopus2-s2.0-85086567198
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086567198&doi=10.1016%2fj.jhydrol.2020.125133&partnerID=40&md5=0fcb898513bc743ab714a773a3cfd3d9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25222
dc.identifier.volume589
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Hydrology
dc.titleRainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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