Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 re used in a deep point of 415 meters. 6-hourly series of atmospheric pressure and wind components from NCEP/NCAR reanalysis data set re also used from ten points over the oceanic area. Correlations and spectral analyse re verified to define the time lag beten the meteorological variables and the coastal sea level response to the occurrences of the extreme atmospheric systems. These correlations and time lags re 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.
['Marilia M. F. de Oliveira', 'Nelson F. F. Ebecken', 'Jorge Luiz Fernandes de Oliveira', 'Luis Manoel Paiva Nunes']
I am writing a research paper on a idea Artificial Neural Network for Predicting Extreme Sea Level Variation Associated with Severe Storms
[{"from": "human", "value": "I am writing a research paper on a idea Artificial Neural Network for Predicting Extreme Sea Level Variation Associated with Severe Storms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 re used in a deep point of 415 meters. 6-hourly series of atmospheric pressure and wind components from NCEP/NCAR reanalysis data set re also used from ten points over the oceanic area. Correlations and spectral analyse re verified to define the time lag beten the meteorological variables and the coastal sea level response to the occurrences of the extreme atmospheric systems. These correlations and time lags re 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."}]