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Modelling of river flow using particle swarm optimized cascade-forward neural networks: A case study of kelantan river in malaysia

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dc.citedby14
dc.contributor.authorHayder G.en_US
dc.contributor.authorSolihin M.I.en_US
dc.contributor.authorMustafa H.M.en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid16644075500en_US
dc.contributor.authorid57217195204en_US
dc.date.accessioned2023-05-29T08:06:51Z
dc.date.available2023-05-29T08:06:51Z
dc.date.issued2020
dc.description.abstractWater resources management in Malaysia has become a crucial issue of concern due to its role in the economic and social development of the country. Kelantan river (Sungai Kelantan) basin is one of the essential catchments as it has a history of flood events. Numerous studies have been conducted in river basin modelling for the prediction of flow and mitigation of flooding events as well as water resource management. This paper presents river flow modelling based on meteorological and weather data in the Sungai Kelantan region using a cascade-forward neural network trained with particle swarm optimization algorithm (CFNNPSO). The result is compared with those trained with the Levenberg�Marquardt (LM) and Bayesian Regularization (BR) algorithm. The outcome of this study indicates that there is a strong correlation between river flow and some meteorological and weather variables (weighted rainfall, average evaporation and temperatures). The correlation scores (R) obtained between the target variable (river flow) and the predictor variables were 0.739, ?0.544, and ?0.662 for weighted rainfall, evaporation, and temperature, respectively. Additionally, the developed nonlinear multivariable regression model using CFNNPSO produced acceptable prediction accuracy during model testing with the regression coefficient (R2), root mean square error (RMSE), and mean of percentage error (MPE) of 0.88, 191.1 cms and 0.09%, respectively. The reliable result and predictive performance of the model is useful for decision makers during water resource planning and river management. The constructed modelling procedure can be adopted for future applications. � 2020 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8670
dc.identifier.doi10.3390/app10238670
dc.identifier.epage16
dc.identifier.issue23
dc.identifier.scopus2-s2.0-85097057225
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097057225&doi=10.3390%2fapp10238670&partnerID=40&md5=0afec6bdf4d00e698ec06fbd972b8cfb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25120
dc.identifier.volume10
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleModelling of river flow using particle swarm optimized cascade-forward neural networks: A case study of kelantan river in malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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