# A Comparative Study of Convolutional and Recurrent Neural Networks for Storm Surge Prediction in Tampa Bay

DevFeed: [A Comparative Study of Convolutional and Recurrent Neural Networks for Storm Surge Prediction in Tampa Bay](<https://devfeed.tech/articles/a-comparative-study-of-convolutional-and-recurrent-neural-networks-for-storm-surge-prediction-in-tampa-bay-103684.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2408.05797>)

Author: Mandana Farhang Ghahfarokhi, Anahita Farhang Ghahfarokhi, Mahta Zamanizadeh, Seyed Hossein Sonbolestan

Published: 2026-10-28T04:00:00Z

Content type: comparison

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>)

Tags: [convolutional-neural-networks](<https://devfeed.tech/tags/convolutional-neural-networks.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [hurricane](<https://devfeed.tech/tags/hurricane.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [physics-ao-ph](<https://devfeed.tech/tags/physics-ao-ph.md>), [prediction](<https://devfeed.tech/tags/prediction.md>), [recurrent-neural-networks](<https://devfeed.tech/tags/recurrent-neural-networks.md>), [storm](<https://devfeed.tech/tags/storm.md>)

## AI overview

Using NOAA wind, pressure, and tide data to predict Tampa Bay water levels, the study reports that LSTM generally outperformed CNN and CNN-LSTM across time windows, with an MSE of 0.006. The authors attribute this result to the inputs being time-based rather than spatial. In their Hurricane Ian analysis, CNN and CNN-LSTM performed slightly better during powerful storms; the authors also report greater reliability for CNN-based models on data absent from training. Perturbation analysis indicated that tide contributed more to predictions than wind or pressure. These are findings reported in the supplied abstract.

## Source excerpt

arXiv:2408.05797v2 Announce Type: replace Abstract: In this paper, we compare the performance of three popular deep learning architectures: Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and CNN-LSTM in water level prediction over different time window sizes. We used wind, pressure, and tide data from National Oceanic and Atmospheric Administration (NOAA) stations. Our results show that the LSTM model generally outperformed the other models in forecasting water level trends. By applying LSTM, we achieved a mean squared error (MSE) of 0.006, making it the most reliable option for real-world use. We predicted water levels using wind, pressure, and tides. Wind, pressure, and tide data are time-based features that lack spatial patterns. Consequently, LSTM performed better than spatial methods, such as CNN. We also investigated Hurricane Ian, which occurred in Tampa Bay in September 2022 and caused a significant storm surge. Our findings show that CNN and CNN-LSTM performed slightly better than LSTMs during powerful storms, such as hurricanes. Hurricanes are seldom observed in training data, and CNN-based models are more reliable when predicting data that were not included in the training. To explain how environmental factors affect the predicted water level, we used the perturbation method. The perturbation shows that the tide is more effective at predicting water levels than wind and pressure. This study demonstrates the potential of deep learning to improve storm surge prediction accuracy.