Towards Predicting Water Levels Using Artificial Neural Networks

For all Ocean related activities it is necessary to predict the actual water levels as accurate as possible. The present work aims at predicting the water levels with a lead time of few hours to a day using the technique of artificial neural networks. Instead of using the previous and current values of observed water level time series directly as input and output the water level anomaly (difference between the observed water level and harmonically predicted tidal level) is calculated for each hour and the ANN model is developed using this time series. The network predicted anomaly is then added to harmonic tidal level to predict the water levels. The models exhibit a high level of prediction accuracy in testing. The models were further tested in real time mode for 8 months as well as during hurricane conditions. The model results were highly in agreement with the observed water levels in both the cases.