Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah
This 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 p...
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| Format: | Article |
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IAEME Publication
2019
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| _version_ | 1825721620072759296 |
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| author | Olivia Muslim, T. Ahmed, Ali Najah Malek, Marlinda Abdul El-Shafie, Ahmed El-Shafie, Amr |
| author_facet | Olivia Muslim, T. Ahmed, Ali Najah Malek, Marlinda Abdul El-Shafie, Ahmed El-Shafie, Amr |
| author_sort | Olivia Muslim, T. |
| collection | UM |
| description | This 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). |
| first_indexed | 2024-03-06T05:53:06Z |
| format | Article |
| id | um.eprints-21082 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:53:06Z |
| publishDate | 2019 |
| publisher | IAEME Publication |
| record_format | dspace |
| spelling | um.eprints-210822019-04-26T07:54:31Z http://eprints.um.edu.my/21082/ Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah Olivia Muslim, T. Ahmed, Ali Najah Malek, Marlinda Abdul El-Shafie, Ahmed El-Shafie, Amr TA Engineering (General). Civil engineering (General) This 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 Publication 2019 Article PeerReviewed Olivia Muslim, T. and Ahmed, Ali Najah and Malek, Marlinda Abdul and El-Shafie, Ahmed and El-Shafie, Amr (2019) Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah. International Journal of Civil Engineering and Technology, 9 (12). pp. 646-656. ISSN 0976-6308, http://www.iaeme.com/MasterAdmin/Journal_uploads/IJCIET/VOLUME_9_ISSUE_12/IJCIET_09_12_070.pdf |
| spellingShingle | TA Engineering (General). Civil engineering (General) Olivia Muslim, T. Ahmed, Ali Najah Malek, Marlinda Abdul El-Shafie, Ahmed El-Shafie, Amr Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah |
| title | Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah |
| title_full | Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah |
| title_fullStr | Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah |
| title_full_unstemmed | Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah |
| title_short | Investigating the impact of wind on sea level rise using multilayer perceptron neural network (MLP-NN) at coastal area, Sabah |
| title_sort | investigating the impact of wind on sea level rise using multilayer perceptron neural network mlp nn at coastal area sabah |
| topic | TA Engineering (General). Civil engineering (General) |
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