Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height
In coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sr...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/8272566 |
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| author | Tzu-Chia Chen Zryan Najat Rashid Biju Theruvil Sayed Arif Sari Ahmed Kateb Jumaah Al-Nussairi Majid Samiee-Zenoozian Mehrdad Shokatian-Beiragh |
| author_facet | Tzu-Chia Chen Zryan Najat Rashid Biju Theruvil Sayed Arif Sari Ahmed Kateb Jumaah Al-Nussairi Majid Samiee-Zenoozian Mehrdad Shokatian-Beiragh |
| author_sort | Tzu-Chia Chen |
| collection | DOAJ |
| description | In coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. The collected subtime series were then utilized as input data for ANN and M5MT in order to determine the wave height. For the sake of the model performance, the daily wind and wave data from the Acoustic Wave and Current (AWAC) sensor for Hambantota Port in 2020 and Sanmen Bay in 2017 were used in this study. The training state utilizes 80% of the available data, while the test state uses 20%. The Root Mean Square Error (RMSE) of the ANN, M5, WANN, and Wavelet-M5 models in the Hambantota Port for the test stage are 0.12, 0.11, 0.04, and 0.06, respectively. While in Sanmen Bay, the RMSE of the ANN, M5, WANN, and Wavelet-M5 models for the test stage are 0.14, 0.16, 0.06, and 0.08, respectively. According to the findings of this study, the accuracy of WANN and Wavelet-M5 hybrid models in evaluating wave height is superior to that of classic ANN and M5MT, and it is recommended that WANN and Wavelet-M5 hybrid models be used to estimate wave height. |
| format | Article |
| id | doaj-art-93350567ef474f318d37ec6e665f8a78 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-93350567ef474f318d37ec6e665f8a782025-02-03T06:42:48ZengWileyAdvances in Civil Engineering1687-80942023-01-01202310.1155/2023/8272566Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave HeightTzu-Chia Chen0Zryan Najat Rashid1Biju Theruvil Sayed2Arif Sari3Ahmed Kateb Jumaah Al-Nussairi4Majid Samiee-Zenoozian5Mehrdad Shokatian-Beiragh6College of Management and DesignTechnical College of InformaticsDepartment of Computer ScienceDepartment of Management Information SystemsAl-Manara College for Medical SciencesDepartment of Water Resources EngineeringDepartment of Water Resources EngineeringIn coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. The collected subtime series were then utilized as input data for ANN and M5MT in order to determine the wave height. For the sake of the model performance, the daily wind and wave data from the Acoustic Wave and Current (AWAC) sensor for Hambantota Port in 2020 and Sanmen Bay in 2017 were used in this study. The training state utilizes 80% of the available data, while the test state uses 20%. The Root Mean Square Error (RMSE) of the ANN, M5, WANN, and Wavelet-M5 models in the Hambantota Port for the test stage are 0.12, 0.11, 0.04, and 0.06, respectively. While in Sanmen Bay, the RMSE of the ANN, M5, WANN, and Wavelet-M5 models for the test stage are 0.14, 0.16, 0.06, and 0.08, respectively. According to the findings of this study, the accuracy of WANN and Wavelet-M5 hybrid models in evaluating wave height is superior to that of classic ANN and M5MT, and it is recommended that WANN and Wavelet-M5 hybrid models be used to estimate wave height.http://dx.doi.org/10.1155/2023/8272566 |
| spellingShingle | Tzu-Chia Chen Zryan Najat Rashid Biju Theruvil Sayed Arif Sari Ahmed Kateb Jumaah Al-Nussairi Majid Samiee-Zenoozian Mehrdad Shokatian-Beiragh Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height Advances in Civil Engineering |
| title | Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height |
| title_full | Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height |
| title_fullStr | Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height |
| title_full_unstemmed | Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height |
| title_short | Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height |
| title_sort | evaluation of hybrid soft computing model s performance in estimating wave height |
| url | http://dx.doi.org/10.1155/2023/8272566 |
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