Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network
Modeling of tsunami wave interaction with coral reefs to date focuses mainly on the process-based numerical models. In this study, an alternative machine learning technique based on the multi-layer perceptron neural network (MLP-NN) is introduced to predict the tsunami-like solitary wave run-up over...
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my.um.eprints.265902022-03-28T01:56:24Z http://eprints.um.edu.my/26590/ Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network Yao, Yu Yang, Xiaoxiao Lai, Sai Hin Chin, Ren Jie TA Engineering (General). Civil engineering (General) Modeling of tsunami wave interaction with coral reefs to date focuses mainly on the process-based numerical models. In this study, an alternative machine learning technique based on the multi-layer perceptron neural network (MLP-NN) is introduced to predict the tsunami-like solitary wave run-up over fringing reefs. Two hydrodynamic forcings (incident wave height, reef-flat water level) and four reef morphologic features (reef width, fore-reef slope, beach slope, reef roughness) are selected as the input variables and wave run-up on the back-reef beach is assigned as the output variable. A validated numerical model based on the Boussinesq equations is applied to provide a dataset consisting of 4096 runs for MLP-NN training and testing. Results analyses show that the MLP-NN consisting of one hidden layer with ten hidden neurons provides the best predictions for the wave run-up. Subsequently, model performances in view of individual input variables are accessed via an analysis of the percentage errors of the predictions. Finally, a mean impact value analysis is also conducted to evaluate the relative importance of the input variables to the output variable. In general, the adopted MLP-NN has high predictive capability for wave run-up over the reef-lined coasts, and it is an alternative but more efficient tool for potential use in tsunami early warning system or risk assessment projects. Springer 2021-05 Article PeerReviewed Yao, Yu and Yang, Xiaoxiao and Lai, Sai Hin and Chin, Ren Jie (2021) Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network. Natural Hazards, 107 (1). pp. 601-616. ISSN 0921-030X, DOI https://doi.org/10.1007/s11069-021-04597-w <https://doi.org/10.1007/s11069-021-04597-w>. 10.1007/s11069-021-04597-w |
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TA Engineering (General). Civil engineering (General) Yao, Yu Yang, Xiaoxiao Lai, Sai Hin Chin, Ren Jie Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network |
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Modeling of tsunami wave interaction with coral reefs to date focuses mainly on the process-based numerical models. In this study, an alternative machine learning technique based on the multi-layer perceptron neural network (MLP-NN) is introduced to predict the tsunami-like solitary wave run-up over fringing reefs. Two hydrodynamic forcings (incident wave height, reef-flat water level) and four reef morphologic features (reef width, fore-reef slope, beach slope, reef roughness) are selected as the input variables and wave run-up on the back-reef beach is assigned as the output variable. A validated numerical model based on the Boussinesq equations is applied to provide a dataset consisting of 4096 runs for MLP-NN training and testing. Results analyses show that the MLP-NN consisting of one hidden layer with ten hidden neurons provides the best predictions for the wave run-up. Subsequently, model performances in view of individual input variables are accessed via an analysis of the percentage errors of the predictions. Finally, a mean impact value analysis is also conducted to evaluate the relative importance of the input variables to the output variable. In general, the adopted MLP-NN has high predictive capability for wave run-up over the reef-lined coasts, and it is an alternative but more efficient tool for potential use in tsunami early warning system or risk assessment projects. |
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Article |
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Yao, Yu Yang, Xiaoxiao Lai, Sai Hin Chin, Ren Jie |
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Yao, Yu Yang, Xiaoxiao Lai, Sai Hin Chin, Ren Jie |
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Yao, Yu |
title |
Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network |
title_short |
Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network |
title_full |
Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network |
title_fullStr |
Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network |
title_full_unstemmed |
Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network |
title_sort |
predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network |
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Springer |
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2021 |
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http://eprints.um.edu.my/26590/ |
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13.160551 |