Deep neural network-based prediction of tsunami wave attenuation by mangrove forests
The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a stagge...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44007/ http://umpir.ump.edu.my/id/eprint/44007/1/Deep%20neural%20network-based%20prediction%20of%20tsunami%20wave%20attenuation.pdf |
| _version_ | 1848827009174601728 |
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| author | Adytia, Didit Tarwidi, Dede Saepudin, Deni Husrin, Semeidi Abdul Rahman, Mohd Kasim Mohd Fakhizan, Romlie Samsudin, Dafrizal |
| author_facet | Adytia, Didit Tarwidi, Dede Saepudin, Deni Husrin, Semeidi Abdul Rahman, Mohd Kasim Mohd Fakhizan, Romlie Samsudin, Dafrizal |
| author_sort | Adytia, Didit |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a staggered grid approximation. The Boussinesq model for wave attenuation is validated using laboratory experiments exhibiting a mean absolute error (MAE) ranging from 0.003 to 0.01. We employ over 40,000 data points generated from the Boussinesq numerical simulations to train the DNN. Efforts are made to optimize hyperparameters and determine the neural network architecture to attain optimal performance during the training process. The prediction results of the DNN model exhibit a coefficient of determination (R2) of 0.99560, an MAE of 0.00118, a root mean squared error (RMSE) of 0.00151, and a mean absolute percentage error (MAPE) of 3 %. When comparing the DNN model with three alternative machine learning models— support vector regression (SVR), multiple linear regression (MLR), and extreme gradient boosting (XGBoost)— the performance of DNN is superior to that of SVR and MLR, but it is similar to XGBoost. • High-accuracy DNN models require hyperparameter optimization and neural network architecture selection. • The error of DNN models in predicting the attenuation of tsunami waves by mangrove forests is less than 3 %. • DNN can serve as an alternate predictive model to empirical formulas or classical numerical models. |
| first_indexed | 2025-11-15T03:53:54Z |
| format | Article |
| id | ump-44007 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:53:54Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-440072025-03-07T04:19:54Z http://umpir.ump.edu.my/id/eprint/44007/ Deep neural network-based prediction of tsunami wave attenuation by mangrove forests Adytia, Didit Tarwidi, Dede Saepudin, Deni Husrin, Semeidi Abdul Rahman, Mohd Kasim Mohd Fakhizan, Romlie Samsudin, Dafrizal Q Science (General) QA Mathematics The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a staggered grid approximation. The Boussinesq model for wave attenuation is validated using laboratory experiments exhibiting a mean absolute error (MAE) ranging from 0.003 to 0.01. We employ over 40,000 data points generated from the Boussinesq numerical simulations to train the DNN. Efforts are made to optimize hyperparameters and determine the neural network architecture to attain optimal performance during the training process. The prediction results of the DNN model exhibit a coefficient of determination (R2) of 0.99560, an MAE of 0.00118, a root mean squared error (RMSE) of 0.00151, and a mean absolute percentage error (MAPE) of 3 %. When comparing the DNN model with three alternative machine learning models— support vector regression (SVR), multiple linear regression (MLR), and extreme gradient boosting (XGBoost)— the performance of DNN is superior to that of SVR and MLR, but it is similar to XGBoost. • High-accuracy DNN models require hyperparameter optimization and neural network architecture selection. • The error of DNN models in predicting the attenuation of tsunami waves by mangrove forests is less than 3 %. • DNN can serve as an alternate predictive model to empirical formulas or classical numerical models. Elsevier B.V. 2024-12 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/44007/1/Deep%20neural%20network-based%20prediction%20of%20tsunami%20wave%20attenuation.pdf Adytia, Didit and Tarwidi, Dede and Saepudin, Deni and Husrin, Semeidi and Abdul Rahman, Mohd Kasim and Mohd Fakhizan, Romlie and Samsudin, Dafrizal (2024) Deep neural network-based prediction of tsunami wave attenuation by mangrove forests. MethodsX, 13 (102791). pp. 1-15. ISSN 2215-0161. (Published) https://doi.org/10.1016/j.mex.2024.102791 https://doi.org/10.1016/j.mex.2024.102791 |
| spellingShingle | Q Science (General) QA Mathematics Adytia, Didit Tarwidi, Dede Saepudin, Deni Husrin, Semeidi Abdul Rahman, Mohd Kasim Mohd Fakhizan, Romlie Samsudin, Dafrizal Deep neural network-based prediction of tsunami wave attenuation by mangrove forests |
| title | Deep neural network-based prediction of tsunami wave attenuation by mangrove forests |
| title_full | Deep neural network-based prediction of tsunami wave attenuation by mangrove forests |
| title_fullStr | Deep neural network-based prediction of tsunami wave attenuation by mangrove forests |
| title_full_unstemmed | Deep neural network-based prediction of tsunami wave attenuation by mangrove forests |
| title_short | Deep neural network-based prediction of tsunami wave attenuation by mangrove forests |
| title_sort | deep neural network-based prediction of tsunami wave attenuation by mangrove forests |
| topic | Q Science (General) QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/44007/ http://umpir.ump.edu.my/id/eprint/44007/ http://umpir.ump.edu.my/id/eprint/44007/ http://umpir.ump.edu.my/id/eprint/44007/1/Deep%20neural%20network-based%20prediction%20of%20tsunami%20wave%20attenuation.pdf |