Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
During every Atlantic hurricane season, storms represent a constant risk to Texan coastal communities and other communities along the Atlantic coast of the United States. A storm surge refers to the abnormal rise of sea water level due to hurricanes and storms; traditionally, hurricane storm surge p...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/5/232 |
| _version_ | 1797601405234577408 |
|---|---|
| author | Cesar Davila Hernandez Jungseok Ho Dongchul Kim Abdoul Oubeidillah |
| author_facet | Cesar Davila Hernandez Jungseok Ho Dongchul Kim Abdoul Oubeidillah |
| author_sort | Cesar Davila Hernandez |
| collection | DOAJ |
| description | During every Atlantic hurricane season, storms represent a constant risk to Texan coastal communities and other communities along the Atlantic coast of the United States. A storm surge refers to the abnormal rise of sea water level due to hurricanes and storms; traditionally, hurricane storm surge predictions are generated using complex numerical models that require high amounts of computing power to be run, which grow proportionally with the extent of the area covered by the model. In this work, a machine-learning-based storm surge forecasting model for the Lower Laguna Madre is implemented. The model considers gridded forecasted weather data on winds and atmospheric pressure over the Gulf of Mexico, as well as previous sea levels obtained from a Laguna Madre ocean circulation numerical model. Using architectures such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) combined, the resulting model is capable of identifying upcoming hurricanes and predicting storm surges, as well as normal conditions in several locations along the Lower Laguna Madre. Overall, the model is able to predict storm surge peaks with an average difference of 0.04 m when compared with a numerical model and an average RMSE of 0.08 for normal conditions and 0.09 for storm surge conditions. |
| first_indexed | 2024-03-11T04:00:27Z |
| format | Article |
| id | doaj.art-3f129ccd707f454ca8e816d90c6569ce |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T04:00:27Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-3f129ccd707f454ca8e816d90c6569ce2023-11-18T00:08:34ZengMDPI AGAlgorithms1999-48932023-04-0116523210.3390/a16050232Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna MadreCesar Davila Hernandez0Jungseok Ho1Dongchul Kim2Abdoul Oubeidillah3Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX 78705, USADepartment of Civil Engineering, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USADepartment of Computer Science, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USADepartment of Civil Engineering, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USADuring every Atlantic hurricane season, storms represent a constant risk to Texan coastal communities and other communities along the Atlantic coast of the United States. A storm surge refers to the abnormal rise of sea water level due to hurricanes and storms; traditionally, hurricane storm surge predictions are generated using complex numerical models that require high amounts of computing power to be run, which grow proportionally with the extent of the area covered by the model. In this work, a machine-learning-based storm surge forecasting model for the Lower Laguna Madre is implemented. The model considers gridded forecasted weather data on winds and atmospheric pressure over the Gulf of Mexico, as well as previous sea levels obtained from a Laguna Madre ocean circulation numerical model. Using architectures such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) combined, the resulting model is capable of identifying upcoming hurricanes and predicting storm surges, as well as normal conditions in several locations along the Lower Laguna Madre. Overall, the model is able to predict storm surge peaks with an average difference of 0.04 m when compared with a numerical model and an average RMSE of 0.08 for normal conditions and 0.09 for storm surge conditions.https://www.mdpi.com/1999-4893/16/5/232machine learningstorm surgehurricaneforecastingCNNLSTM |
| spellingShingle | Cesar Davila Hernandez Jungseok Ho Dongchul Kim Abdoul Oubeidillah Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre Algorithms machine learning storm surge hurricane forecasting CNN LSTM |
| title | Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre |
| title_full | Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre |
| title_fullStr | Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre |
| title_full_unstemmed | Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre |
| title_short | Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre |
| title_sort | machine learning based model for hurricane storm surge forecasting in the lower laguna madre |
| topic | machine learning storm surge hurricane forecasting CNN LSTM |
| url | https://www.mdpi.com/1999-4893/16/5/232 |
| work_keys_str_mv | AT cesardavilahernandez machinelearningbasedmodelforhurricanestormsurgeforecastinginthelowerlagunamadre AT jungseokho machinelearningbasedmodelforhurricanestormsurgeforecastinginthelowerlagunamadre AT dongchulkim machinelearningbasedmodelforhurricanestormsurgeforecastinginthelowerlagunamadre AT abdouloubeidillah machinelearningbasedmodelforhurricanestormsurgeforecastinginthelowerlagunamadre |