South Texas coastal area storm surge model development and improvement

Davila, Sara; Hernandez, Cesar Davila; Flores, Martin; Ho, Jungseok

doi:10.3934/geosci.2020016

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The details of the ADCIRC model utilized to create the database of results for the Laguna Madre can be seen in the following study [27]. To execute ADCIRC and create the dataset necessary for training the machine learning model, it is necessary to develop two files for each simulation.…”

Section: Adcirc Numerical Model Databasementioning

confidence: 99%

Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre

Hernandez

Kim

et al. 2023

Algorithms

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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.

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Section: Adcirc Numerical Model Databasementioning

confidence: 99%

Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre

Hernandez

Kim

et al. 2023

Algorithms

Self Cite

View full text Add to dashboard Cite

show abstract

Automation and Coupling of Models for Coastal Flood Forecasting in South Texas

et al. 2021

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Forecasting natural disasters such as inundations can be of great help for emergency bodies and first responders. In coastal communities, this risk is often associated with storm surge. To produce flood forecasts for coastal communities, a system must incorporate models capable of simulating such events based on forecasted weather conditions. In this work, a system for forecasting inundations based predominantly on storm surge is explored. An automation and a coupling strategy were implemented to produce forecasted flood maps automatically. The system leverages an ocean circulation model and a channel water flow model to estimate flood events in South Texas specially alongside the Lower Laguna Madre. The system around the models is implemented using Python and the meteorological forcing input is obtained from weather forecasting models maintained by the National Oceanic and Atmospheric Administration. The forecasted weather data retrieval, data processing and automation of the models are successful, and the complete stack of software can be deployed locally or in cloud solutions to accelerate computations. The resulting system performs as expected and successfully produces flood maps automatically providing vital information for flood emergency management in coastal communities.

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South Texas coastal area storm surge model development and improvement

Cited by 2 publications

References 8 publications

Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre

Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre

Automation and Coupling of Models for Coastal Flood Forecasting in South Texas

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