Publication:
Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting

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dc.citedby7
dc.contributor.authorWei Y.en_US
dc.contributor.authorHashim H.en_US
dc.contributor.authorChong K.L.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid58169750800en_US
dc.contributor.authorid56800153400en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:18:21Z
dc.date.available2024-10-14T03:18:21Z
dc.date.issued2023
dc.description.abstractThe deterministic approach, which utilizes the gradient information in the search process, is prone to trapping at local minima, primarily due to the presence of saddle points and local minima in the non-convex objective function of an artificial neural network (ANN). This study investigated the efficacy of a hybrid model that adopted a meta-heuristic algorithm (MHA) as an optimizer to extend the training ANN method, from a gradient-based to a stochastic population-based approach for streamflow forecasting. In the latter, parameter tuning utilizing the design of experiment (DOE) technique, has become an integral element in the optimization process due to reliance on their parameters. For model convenience, a wavelet transform was employed to decompose the series into sub-series. The empirical studies of MHA performance showed that the hybrid MHA-ANN was superior for streamflow forecasting, especially with the firefly algorithm that had an average RMSE = 96.06, an improvement of approximately 17% over the gradient-based ANN (RMSE = 113.92). However, among the adopted MHAs, not all are compatible with optimizing the ANN for streamflow forecasting, thus requiring a thorough study as performance varies from case to case. Two additional statistical tests, such as the Kruskal-Wallis H test and the Mann-Whitney U test, further validated such disparity in the MHA�s performance. � 2023, Korean Society of Civil Engineers.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s12205-023-0821-6
dc.identifier.epage2312
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85151518306
dc.identifier.spage2297
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151518306&doi=10.1007%2fs12205-023-0821-6&partnerID=40&md5=070472e797481fef213fc82d40c161d7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34190
dc.identifier.volume27
dc.pagecount15
dc.publisherKorean Society of Civil Engineersen_US
dc.sourceScopus
dc.sourcetitleKSCE Journal of Civil Engineering
dc.subjectMachine learning
dc.subjectMeta-heuristic algorithms
dc.subjectOptimization
dc.subjectStatistical tests
dc.subjectTime series forecasting
dc.subjectWavelet transform
dc.subjectDesign of experiments
dc.subjectForecasting
dc.subjectHeuristic algorithms
dc.subjectHeuristic methods
dc.subjectMachine learning
dc.subjectNeural networks
dc.subjectStatistical tests
dc.subjectStatistics
dc.subjectStochastic models
dc.subjectStochastic systems
dc.subjectStream flow
dc.subjectWavelet transforms
dc.subjectDeterministic approach
dc.subjectGradient based
dc.subjectLocal minimums
dc.subjectMachine-learning
dc.subjectMeta-heuristics algorithms
dc.subjectOptimisations
dc.subjectPerformance
dc.subjectStreamflow forecasting
dc.subjectTime series forecasting
dc.subjectWavelets transform
dc.subjectOptimization
dc.titleInvestigation of Meta-heuristics Algorithms in ANN Streamflow Forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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