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

artificial intelligence; artificial neural network; error analysis; hydrological cycle; performance assessment; prediction; sea level change; sensitivity analysis; wind direction; Malaysia

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Main Authors: Muslim T.O.B., Ahmed A.N., Malek M.A.
Other Authors: 57215584776
Format: Article
Published: Thai Society of Higher Eduation Institutes on Environment 2023
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spelling my.uniten.dspace-257302023-05-29T16:13:29Z Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction Muslim T.O.B. Ahmed A.N. Malek M.A. 57215584776 57214837520 55636320055 artificial intelligence; artificial neural network; error analysis; hydrological cycle; performance assessment; prediction; sea level change; sensitivity analysis; wind direction; Malaysia Sea 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. Final 2023-05-29T08:13:29Z 2023-05-29T08:13:29Z 2020 Article 10.14456/ea.2020.4 2-s2.0-85085024474 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085024474&doi=10.14456%2fea.2020.4&partnerID=40&md5=d4da0c31d70ced4370ebc9e2130ecb0f https://irepository.uniten.edu.my/handle/123456789/25730 13 1 41 52 Thai Society of Higher Eduation Institutes on Environment Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description artificial intelligence; artificial neural network; error analysis; hydrological cycle; performance assessment; prediction; sea level change; sensitivity analysis; wind direction; Malaysia
author2 57215584776
author_facet 57215584776
Muslim T.O.B.
Ahmed A.N.
Malek M.A.
format Article
author Muslim T.O.B.
Ahmed A.N.
Malek M.A.
spellingShingle Muslim T.O.B.
Ahmed A.N.
Malek M.A.
Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction
author_sort Muslim T.O.B.
title Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction
title_short Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction
title_full Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction
title_fullStr Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction
title_full_unstemmed Performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction
title_sort performance of multi-layer perceptron-neural network versus random forest regression for sea level rise prediction
publisher Thai Society of Higher Eduation Institutes on Environment
publishDate 2023
_version_ 1806424253209247744
score 13.211869

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