Improving sea level prediction in coastal areas using machine learning techniques
The objective of the current study is to investigate the effectiveness of specifically the Support Vector Machine (SVM) and the k-Nearest Neighbors (kNN) models for sea level prediction. The SVM and kNN models are compared using the predicted data determined by the machine learning model's perf...
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Ain Shams University
2025
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| author | Latif S.D. Almubaidin M.A. Shen C.G. Sapitang M. Birima A.H. Ahmed A.N. Sherif M. El-Shafie A. |
| author2 | 57216081524 |
| author_facet | 57216081524 Latif S.D. Almubaidin M.A. Shen C.G. Sapitang M. Birima A.H. Ahmed A.N. Sherif M. El-Shafie A. |
| author_sort | Latif S.D. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The objective of the current study is to investigate the effectiveness of specifically the Support Vector Machine (SVM) and the k-Nearest Neighbors (kNN) models for sea level prediction. The SVM and kNN models are compared using the predicted data determined by the machine learning model's performance. Thirteen models were trained precisely and properly throughout the machine learning process. The results showed that SVM models provide good performance during the training process and attained relatively poor performance during testing process. On the other hand, the KNN model showed consistent performance for both training and testing process. Regarding the effectiveness of different kernels of the SVM algorithm, the Radial Basis Function (RBF) kernel is the most suitable, which provides the finest analysis for the sea level rise dataset and acceptable values for RSME, MAE, and R2. ? 2024 THE AUTHORS |
| format | Article |
| id | my.uniten.dspace-36398 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Ain Shams University |
| record_format | dspace |
| spelling | my.uniten.dspace-363982025-03-03T15:42:14Z Improving sea level prediction in coastal areas using machine learning techniques Latif S.D. Almubaidin M.A. Shen C.G. Sapitang M. Birima A.H. Ahmed A.N. Sherif M. El-Shafie A. 57216081524 57476845900 59210032500 57215211508 23466519000 57214837520 7005414714 16068189400 Coastal zones Learning systems Motion compensation Nearest neighbor search Radial basis function networks Sea level Coastal area Flood modeling K-near neighbor K-nearest neighbour models Machine learning techniques Machine-learning Support vector machine Support vectors machine Testing process Training process Support vector machines The objective of the current study is to investigate the effectiveness of specifically the Support Vector Machine (SVM) and the k-Nearest Neighbors (kNN) models for sea level prediction. The SVM and kNN models are compared using the predicted data determined by the machine learning model's performance. Thirteen models were trained precisely and properly throughout the machine learning process. The results showed that SVM models provide good performance during the training process and attained relatively poor performance during testing process. On the other hand, the KNN model showed consistent performance for both training and testing process. Regarding the effectiveness of different kernels of the SVM algorithm, the Radial Basis Function (RBF) kernel is the most suitable, which provides the finest analysis for the sea level rise dataset and acceptable values for RSME, MAE, and R2. ? 2024 THE AUTHORS Final 2025-03-03T07:42:14Z 2025-03-03T07:42:14Z 2024 Article 10.1016/j.asej.2024.102916 2-s2.0-85197902654 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197902654&doi=10.1016%2fj.asej.2024.102916&partnerID=40&md5=7e4135fd5e9fa1bac860ae4994459361 https://irepository.uniten.edu.my/handle/123456789/36398 15 9 102916 All Open Access; Gold Open Access Ain Shams University Scopus |
| spellingShingle | Coastal zones Learning systems Motion compensation Nearest neighbor search Radial basis function networks Sea level Coastal area Flood modeling K-near neighbor K-nearest neighbour models Machine learning techniques Machine-learning Support vector machine Support vectors machine Testing process Training process Support vector machines Latif S.D. Almubaidin M.A. Shen C.G. Sapitang M. Birima A.H. Ahmed A.N. Sherif M. El-Shafie A. Improving sea level prediction in coastal areas using machine learning techniques |
| title | Improving sea level prediction in coastal areas using machine learning techniques |
| title_full | Improving sea level prediction in coastal areas using machine learning techniques |
| title_fullStr | Improving sea level prediction in coastal areas using machine learning techniques |
| title_full_unstemmed | Improving sea level prediction in coastal areas using machine learning techniques |
| title_short | Improving sea level prediction in coastal areas using machine learning techniques |
| title_sort | improving sea level prediction in coastal areas using machine learning techniques |
| topic | Coastal zones Learning systems Motion compensation Nearest neighbor search Radial basis function networks Sea level Coastal area Flood modeling K-near neighbor K-nearest neighbour models Machine learning techniques Machine-learning Support vector machine Support vectors machine Testing process Training process Support vector machines |
| url_provider | http://dspace.uniten.edu.my/ |