Tidal forecasting using RNN in Bahia Blanca estuary, Argentina

Abstract

In recent years, the availability of accurate ocean tide models has become increasingly important, as tides are the main contributor to disposal and movement of sediments, tracers and pollutants, and also due to a wide range of offshore applications in engineering, environmental observations, exploration and oceanography. Tides can be conventionally predicted by harmonic analysis, which is the superposition of many sinusoidal constituents with amplitudes and frequencies determined by a local analysis of the measured tide. However, accurate predictions of tide levels could not be obtained without a large number of tide measurements by the harmonic method. An application of the back-propagation artifcial neural network using long-term and short-term measuring data is presented in this paper. On site tidal level data at Ingeniero White harbor in the inner part of Bahia Blanca estuary, Argentina, will be used to test the performance of the present model. Comparison with conventional harmonic methods indicates that the back-propagation neural network model also predicts accurately the long-term tidal levels.