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Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah

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dc.citedby3
dc.contributor.authorOlivia Muslim T.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorEL-Shafie A.en_US
dc.contributor.authorid57205233082en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid57207789882en_US
dc.date.accessioned2023-05-29T06:50:02Z
dc.date.available2023-05-29T06:50:02Z
dc.date.issued2018
dc.description.abstractThis study investigating the impact of wind on sea level rise (SLR) using Multilayer Perceptron Neural Network (MLP-NN) at Coastal Area, Sabah. The mean sea level (MSL) and four meteorology parameters namely; wind direction (WD), wind speed (WS), rainfall and mean cloud cover. These meteorological parameter and MSL were monitored regularly each month over a period from January 2007 to December 2016 at three different locations which is Kudat, Kota Kinabalu and Sandakan. Due to small amount of data set, both method the input data were divided into 80 % for training and 20% for testing data respectively.In this study, two scenarios were introduced; the scenario 1 (with wind) WD and WS as input parameter while scenario 2 (without wind)rainfall and mean cloud cover to predict sea level at each stations. Then by using previous monthly sea water level records the model was performed by predicting SLR for1 year, 5 years, 10 years, 30 years, and 50 years ahead in the future. The performance of the models was evaluated according to three statistical indices in terms of the correlation coefficient (R), root mean square error (RMSE) and scatter index (SI). Investigation results indicate that, when compared to measurements, for 50 years prediction, all three models in scenario 2 perform well (with average values of R = 0.6, RMSE = 0.2 cm and SI = 0.4). � IAEME Publicationen_US
dc.description.natureFinalen_US
dc.identifier.epage656
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85059245752
dc.identifier.spage646
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059245752&partnerID=40&md5=67bc1c3deb16af369dc17ac008649778
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23515
dc.identifier.volume9
dc.publisherIAEME Publicationen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Civil Engineering and Technology
dc.titleInvestigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabahen_US
dc.typeArticleen_US
dspace.entity.typePublication
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