Consequence prediction of deck tank leakage on LNG powered ship using RAdam Bi-LSTM

To Predict the consequences of deck storage tank leakage on a LNG powered ship is difficult, time consuming and costly. The RAdam-LSTM is introduced to handle the problem. A set of data for network training is produced by means of software FLACS. The predictions are evaluated with their determination coefficient. The parameters with different Bi-LSTM networks are calculated and compared, including the following parameters: rectified linear unit（Relu）, sigmoid, tanh, Adam, and SGD. Based on the calculation and comparison, the most accurate and suitable Bi-LSTM network is selected. The advantage of the Relu activated Bi-LSTM model is verified through comparing it to RNN, ordinary LSTM and ordinary Bi-LSTM. Experiment shows that the R² achieved by the Bi-LSTM network is 4.5% and 1.5% higher than that from the other two models, respectively.