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Investigating the influence of meteorological parameters on the accuracy of sea-level prediction models in Sabah, Malaysia

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dc.citedby15
dc.contributor.authorMuslim T.O.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorIbrahim R.K.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorSapitang M.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57215584776en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57188832586en_US
dc.contributor.authorid57207789882en_US
dc.contributor.authorid57215211508en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:11:14Z
dc.date.available2023-05-29T08:11:14Z
dc.date.issued2020
dc.descriptionartificial intelligence; artificial neural network; cloud cover; meteorology; prediction; sea level change; East Malaysia; Kota Kinabalu; Kudat; Malaysia; Sabahen_US
dc.description.abstractThis study aims to investigate the impact of meteorological parameters such as wind direction, wind speed, rainfall, and mean cloud cover on sea-level rise projections for different time horizons-2019, 2023, 2028, 2048, and 2068-at three stations located in Kudat, Sandakan, and Kota Kinabalu, which are districts in the state of Sabah, Malaysia. Herein, two different scenarios, scenario1 (SC1) and scenario2 (SC2), were investigated, with each scenario comprising a different combination of input parameters. This study proposes two artificial intelligence techniques: a multilayer perceptron neural network (MLP-ANN) and an adaptive neuro-fuzzy inference system (ANFIS). Furthermore, three evaluation indexes were adopted to assess the performance of the proposed models. These indexes are the correlation coefficient, root mean square error, and scatter index. The trial and error method were used to tune the hyperparameters: the number of neurons in the hidden layer, training algorithms, transfer and activation functions, and number and shape of the membership function for the proposed models. Results show that for the above mentioned three stations, the ANFIS model outperformed MLP-ANN by 0.740%, 6.23%, and 9.39%, respectively. To assess the uncertainties of the best model, ANFIS, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPUs) and the band width of 95 percent confidence intervals (d-factors) are selected. The obtained values bracketed by 95PPUs are show about 75.2%, 77.4%, 76.8% and the d-factor has a value of 0.27, 0.21 and 0.23 at Kudat, Sandakan and Kota Kinabalu stations, respectively. A comparison between the two scenarios shows that SC1 achieved a high level of accuracy on Kudat and Sandakan data, whereas SC2 outperformed SC1 on Kota Kinabalu data. � 2020 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1193
dc.identifier.doi10.3390/su12031193
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85081242286
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081242286&doi=10.3390%2fsu12031193&partnerID=40&md5=0806d1b05da5b660bf387a9c08a53922
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25581
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleInvestigating the influence of meteorological parameters on the accuracy of sea-level prediction models in Sabah, Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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