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Abstract: Accurate predictions of storm surge and surge deviation are essential for industrial activities in coastal areas. Usually numerical hydrodynamic models or empirical methods are used to estimate the storm surge. This paper proposes an alternative back-propagation neural network (BPN) approach to forecast the storm surge and surge deviation. The prediction of storm surge from a previous typhoon is used as a training set to form predictions for the next event. Wind velocity, wind direction, atmospheric pressure and astronomical tide were selected as inputs in the neural network. The observations obtained during three typhoons from four stations in Taiwan were used to illustrate performance of the BPN model. Comparisons with numerical methods indicate that the storm surge and surge deviation can be efficiently predicted using BPN. [Copyright &y& Elsevier]

Accurate tidal prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. The harmonic tidal level is conventionally used to predict tide levels. However, determination of the tidal components using the spectral analysis requires a long-term tidal level record (more than one year [Handbook of coastal and ocean engineering 1 (1990) 534]). In addition, calculating the coefficients abbreviated of tide component using the least-squares method also requires a large database of tide measurements. This paper presents an application of the artificial neural network for predicting and supplementing the long-term tidal-level using the short term observed data. On site, tidal-level data at Taichung Harbor in Taiwan will be used to test the performance of the artificial neural network model. The results show that the tidal levels over a long duration can be efficiently predicted or supplemented using only a short-term tidal record. [Copyright &y& Elsevier]