Marilia M. F. de Oliveira

Neural Network Model to Predict a Storm Surge

Abstract The southeastern coast of Brazil is frequently affected by meteorological disturbances such as cold fronts, which are sometimes associated with intense extratropical cyclones. These disturbances cause oscillations on the sea surface, generating low-frequency motions. The relationship of these meteorologically driven forces in low frequency to the storm-surge event is investigated in this work. A method to predict coastal sea level variations related to meteorological events that use a neural network model (NNM) is presented here. Pressure and wind values from NCEP–NCAR reanalysis data and tide gauge time series from the Cananeia reference station in Sao Paulo State, Brazil, were used to analyze the relationship between these variables and to use them as input to the model. Meteorological influences in the sea level fluctuations can be verified by filtering the astronomical tide frequencies for periods lower than tidal cycles (periods higher than 24 h). Thus, a low-pass filter was applied in the t...

Artificial Neural Network for Predicting Extreme Sea Level Variation Associated with Severe Storms

This paper presents an Artificial Neural Network (ANN) model developed to predict extreme sea level variation in Santos basin on the Southeast region of Brazil, related to the passage of frontal systems associated with cyclones. A methodology was developed and applied to Petrobras water deep data set. Hourly time series of water level were used in a deep point of 415 meters. 6-hourly series of atmospheric pressure and wind components from NCEP/NCAR reanalysis data set were also used from ten points over the oceanic area. Correlations and spectral analyse were verified to define the time lag between the meteorological variables and the coastal sea level response to the occurrences of the extreme atmospheric systems. These correlations and time lags were used as input variables of the ANN model. This model was compared with multiple linear regression (MLR) and presented the best performance, generalizing the effect of the atmospheric interactions on extreme sea level variations.