Abstract: Information on site specific measured oceanographic data such as wind generated waves, currents is seldom available and plays an important role in offering solution to various coastal engineering problems. Due to unanticipated difficulties during field measurement campaigns, it has been observed that measured data for some specific duration is missing or lost. In such circumstances, data driven methods are essential to predict the missing data and its use to calibrate the physical model and to offer engineering solution to the project. Present study investigates the applicability of one of the popular recently developed artificial intelligence (AI) model named as long short-term memory (LSTM) in predicting current data in a macro tide dominated Thane creek and the significant wave height (Hs) for Floating Storage & Regasification Unit (FSRU) structure. The results indicate that LSTM networks formed based on data of 7/15 days of current/tide, can predict current data for 8/10 days with root mean square error (RMSE) of 0.091 and 0.09 respectively. Study also reveals that for location in Thane creek, LSTM prepared on the basis of 8 days of Hs data is able to predict Hs for 33 hours with RMSE value of 0.14. Based on the predicted data, current data is used to calibrate the physical tidal model to determine flow conditions for proposed FSRU in Thane creek and also operable condition (Hs). The study reveals that FSRU needs to be aligned at 33° N and hence LSTM network was found to be useful in design of waterfront structures.
Keywords: Artificial intelligence, current, LSTM, oceanographic data, significant wave height.
Title: Prediction of oceanographic data using LSTM network: A case study for FSRU in Arabian Sea
Author: A. Basu, A.A. Purohit, K.A. Chavan
International Journal of Civil and Structural Engineering Research
ISSN 2348-7607 (Online)
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