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Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction

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dc.citedby2
dc.contributor.authorMuslim T.O.B.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorid57215584776en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55636320055en_US
dc.date.accessioned2023-05-29T08:13:29Z
dc.date.available2023-05-29T08:13:29Z
dc.date.issued2020
dc.descriptionartificial intelligence; artificial neural network; error analysis; hydrological cycle; performance assessment; prediction; sea level change; sensitivity analysis; wind direction; Malaysiaen_US
dc.description.abstractSea Level Rise (SLR) is one of the most difficult elements to predict in the hydrological cycle. 12% of the area of Peninsular Malaysia, where the western low plains of muddy sediment are home to 2.5 million people, is vulnerable to flooding. In this study, two Artificial Intelligence (AI) techniques were used to predict SLR, namely, the Multi-Layer Perceptron Neural Network (MLP-NN) and Random Forest Regression (RFR) techniques. This studied, two cases were presented. The first case (Case 1) was to establish the prediction model for SLR by a monthly data set, while the second case (Case 2) was by means of a cyclical data set. From sensitivity analysis result, it was found that the most effective meteorological input parameters were rainfall (mm) and wind direction (degree). The performance of the models was evaluated according to three statistical indices in terms of the correlation coeffificient (R), root mean square error (RMSE) and scatter index (SI). A comparison of the results of the MLP-NN and RFR showed that the MLP-NN performed better than the latter as the R obtained in Case 2 of the MLP-NN was 0.733 with 65.652 and 2.735 for RMSE and SI respectively. Meanwhile, accuracy improvement percentage (%AI) was 8%. � 2020, Thai Society of Higher Eduation Institutes on Environment. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14456/ea.2020.4
dc.identifier.epage52
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85085024474
dc.identifier.spage41
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085024474&doi=10.14456%2fea.2020.4&partnerID=40&md5=d4da0c31d70ced4370ebc9e2130ecb0f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25730
dc.identifier.volume13
dc.publisherThai Society of Higher Eduation Institutes on Environmenten_US
dc.sourceScopus
dc.sourcetitleEnvironmentAsia
dc.titlePerformance of multi-layer perceptron-neural network versus random forest regression for sea level rise predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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