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

• Published in: Algorithms Vol. 16; no. 5; p. 232
• Main Authors: Davila Hernandez, Cesar, Ho, Jungseok, Kim, Dongchul, Oubeidillah, Abdoul
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 28.04.2023
• Subjects: Analysis; Artificial intelligence; Artificial neural networks; Atmospheric models; CNN; Floods; forecasting; Gulf of Mexico; hurricane; Hurricane forecasting; Hurricanes; Infrastructure; Literature reviews; LSTM; Machine learning; Mathematical models; Meteorological data; Neural networks; Numerical models; Ocean circulation; Ocean currents; Ocean models; Philippines; Rain; Seawater; storm surge; Storm surges; Storms; Texas; Tidal waves; Water levels; Weather; Weather forecasting; Texas; Philippines; Gulf of Mexico; United States > US; Laguna Madre
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a16050232

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.