Optimization of operative wave forecasting by artificial intelligence

Abstract

Sea state forecasting is of primary importance for marine operations related to offshore oil&gas industry. Operative decisions are based on marine weather forecasts. For this reason, at least once a day a bulletin of marine weather forecasts is delivered on board. Bulletins give a very good general overview of weather for the next days, while sometime they are not accurate relatively to the forecasted seastate values or their time of occurrence. In this work has been developed an Artificial Intelligence model named time delayed neural networks (TDNN) to improve the accuracy of short term sea state forecasting, combining bulletins forecasts with buoy measurements. The TDNN parameters are optimized by Genetic Algorithms. It has been found that the optimized TDNN produces very good forecasts, always outperforming bulletins, at least up to 24 hours ahead. Two configurations of TDNN are analyzed: in the first one, TDNN is trained with only buoy measurements, while in the second one TDNN is trained with both buoy and bulletin data. For 24 hours lead time, the combined use of buoy measurements and bulletins as input has been demonstrated to give better results than the use of only buoy measurements.