Using artificial neural networks to predict storm surge in the North Sea and the Thames Estuary
Using artificial neural networks to predict storm surge in the North Sea and the Thames Estuary
An artificial neural network (ANN) was developed to predict storm surge magnitudes and arrival times at selected locations in the North Sea. The model predicts storm surges based solely on past measured water level residuals at one or more tidal stations. The research focuses on the performance of the model at the Sheerness tide station near the entrance of the River Thames in the UK. To take advantage of the specificity of surge propagation in the North Sea, the ANN uses input from both the target station and an additional station located where the peak of the storm surge has just passed. The ANN is trained to relate surge at the primary station from measured surge at a secondary station. The optimal secondary location is correlated to the forecast interval and the storm surge’s propagation time between the secondary and primary station.
This research further explores new forecasting methods using ANN ensembles to reduce variance and minimize error. The ensemble forecasting method averages results from multiple ANN models trained based on different model initializations.
A significant result of this research is the ANN’s ability to accurately predict maximum water elevations. A single ANN model had a 4-hour forecast error of 0.017 m, while a simple [1,1] ensemble model using 20 repetitions performed better with an average 4-hour forecast error of 0.008 m. When over-training is included to reduce the model bias, the error is further reduced to 0.004 m. ANN ensemble model performances for predicting maximum storm surge were however less impressive. Best results were obtained for ensembles of [30,1] models with an average 4-hour forecast error of 0.68 m.
University of Southampton
Prouty, Daniel Bruce
ee2c727f-b9d4-4586-bf1c-83b2843cf0f7
2007
Prouty, Daniel Bruce
ee2c727f-b9d4-4586-bf1c-83b2843cf0f7
Prouty, Daniel Bruce
(2007)
Using artificial neural networks to predict storm surge in the North Sea and the Thames Estuary.
University of Southampton, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
An artificial neural network (ANN) was developed to predict storm surge magnitudes and arrival times at selected locations in the North Sea. The model predicts storm surges based solely on past measured water level residuals at one or more tidal stations. The research focuses on the performance of the model at the Sheerness tide station near the entrance of the River Thames in the UK. To take advantage of the specificity of surge propagation in the North Sea, the ANN uses input from both the target station and an additional station located where the peak of the storm surge has just passed. The ANN is trained to relate surge at the primary station from measured surge at a secondary station. The optimal secondary location is correlated to the forecast interval and the storm surge’s propagation time between the secondary and primary station.
This research further explores new forecasting methods using ANN ensembles to reduce variance and minimize error. The ensemble forecasting method averages results from multiple ANN models trained based on different model initializations.
A significant result of this research is the ANN’s ability to accurately predict maximum water elevations. A single ANN model had a 4-hour forecast error of 0.017 m, while a simple [1,1] ensemble model using 20 repetitions performed better with an average 4-hour forecast error of 0.008 m. When over-training is included to reduce the model bias, the error is further reduced to 0.004 m. ANN ensemble model performances for predicting maximum storm surge were however less impressive. Best results were obtained for ensembles of [30,1] models with an average 4-hour forecast error of 0.68 m.
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Published date: 2007
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Local EPrints ID: 466249
URI: http://eprints.soton.ac.uk/id/eprint/466249
PURE UUID: 2d466dd1-5b79-40da-ab0d-5fe315b62fd8
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Date deposited: 05 Jul 2022 04:55
Last modified: 16 Mar 2024 20:35
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Author:
Daniel Bruce Prouty
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