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Rainfall modeling using two different neural networks improved by metaheuristic algorithms

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dc.citedby2
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorAl-Ansari N.en_US
dc.contributor.authorGhorbani M.A.en_US
dc.contributor.authorBhat S.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorShahid S.en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid59157643200en_US
dc.contributor.authorid58715013100en_US
dc.contributor.authorid56432130100en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57195934440en_US
dc.date.accessioned2024-10-14T03:17:23Z
dc.date.available2024-10-14T03:17:23Z
dc.date.issued2023
dc.description.abstractRainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including�multilayer perceptron (MLP)�Henry gas solubility optimization (HGSO), MLP�bat algorithm (MLP�BA), MLP�particle swarm optimization (MLP�PSO), radial basis neural network function (RBFNN)�HGSO, RBFNN�PSO, and RBFGNN�BA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and Nash�Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models� performance. The results indicated that the MLP�HGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLP�HGSO, MLP�PSO, MLP�BA, RBFNN�HGSO, RBFNN�BA, and RBFNN�PSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS values�were 0.90�0.23, 0.83�0.29, 0.85�0.25, 0.87�0.27, 0.81�0.31, and 0.80�0.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLP�HGSO, MLP�PSO, MLP�BA, RBFNN�HGSO, RBFNN�BA, and RBFNN�PSO during testing processes and the corresponding NSE�PBIAS values were 0.92�0.22, 0.85�0.28, 0.89�0.26, 0.91�0.25, 0.83�0.31, 0.82�0.32, respectively. Based on the outputs of the MLP�HGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLP�HGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLP�HGSO was selected as an effective rainfall prediction model. � 2023, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo112
dc.identifier.doi10.1186/s12302-023-00818-0
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85179670913
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85179670913&doi=10.1186%2fs12302-023-00818-0&partnerID=40&md5=761f23db03047f0a90d122bdf0ccb6ff
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33886
dc.identifier.volume35
dc.publisherSpringeren_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleEnvironmental Sciences Europe
dc.subjectMarkov chain
dc.subjectMLP
dc.subjectProbability matrix
dc.subjectRainfall modelling
dc.subjectRBFNN
dc.subjectMalaysia
dc.subjectForecasting
dc.subjectMarkov processes
dc.subjectMultilayer neural networks
dc.subjectParticle swarm optimization (PSO)
dc.subjectSoft computing
dc.subjectWater management
dc.subjectGas solubility
dc.subjectMean absolute error
dc.subjectMultilayers perceptrons
dc.subjectNetwork functions
dc.subjectOptimisations
dc.subjectProbability matrixes
dc.subjectRadial base neural network function
dc.subjectRadial basis neural networks
dc.subjectRainfall modelling
dc.subjectTesting process
dc.subjectclimate modeling
dc.subjectclimate prediction
dc.subjectMarkov chain
dc.subjectoptimization
dc.subjectrainfall
dc.subjectRain
dc.titleRainfall modeling using two different neural networks improved by metaheuristic algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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