Articles | Volume 12, issue 12
Nat. Hazards Earth Syst. Sci., 12, 3799–3809, 2012
https://doi.org/10.5194/nhess-12-3799-2012
Nat. Hazards Earth Syst. Sci., 12, 3799–3809, 2012
https://doi.org/10.5194/nhess-12-3799-2012

Research article 21 Dec 2012

Research article | 21 Dec 2012

Predicting typhoon-induced storm surge tide with a two-dimensional hydrodynamic model and artificial neural network model

W.-B. Chen1, W.-C. Liu2, and M.-H. Hsu3 W.-B. Chen et al.
  • 1Supercomputing Research Center, National Cheng Kung University, 70101 Tainan, Taiwan
  • 2Department of Civil and Disaster Prevention Engineering, National United University, 36003 Miaoli, Taiwan
  • 3Department of Bioenvironmental Systems Engineering, National Taiwan University, 10617 Taipei, Taiwan

Abstract. Precise predictions of storm surges during typhoon events have the necessity for disaster prevention in coastal seas. This paper explores an artificial neural network (ANN) model, including the back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) algorithms used to correct poor calculations with a two-dimensional hydrodynamic model in predicting storm surge height during typhoon events. The two-dimensional model has a fine horizontal resolution and considers the interaction between storm surges and astronomical tides, which can be applied for describing the complicated physical properties of storm surges along the east coast of Taiwan. The model is driven by the tidal elevation at the open boundaries using a global ocean tidal model and is forced by the meteorological conditions using a cyclone model. The simulated results of the hydrodynamic model indicate that this model fails to predict storm surge height during the model calibration and verification phases as typhoons approached the east coast of Taiwan. The BPNN model can reproduce the astronomical tide level but fails to modify the prediction of the storm surge tide level. The ANFIS model satisfactorily predicts both the astronomical tide level and the storm surge height during the training and verification phases and exhibits the lowest values of mean absolute error and root-mean-square error compared to the simulated results at the different stations using the hydrodynamic model and the BPNN model. Comparison results showed that the ANFIS techniques could be successfully applied in predicting water levels along the east coastal of Taiwan during typhoon events.

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