Prediction with coastal environments and marine diesel engine data based on ship intelligent platform

An intelligent platform prototype is established for a coastal environment monitoring ship. LSTM and GBDT methods are developed for pH value and fuel consumption prediction in the intelligent platform. The results of applying the general prediction algorithms to actual environments' data and marine diesel engine data are reported. GBDT has the best predictive results with the smallest error. SVM and SVR have similar prediction effects, while FNN has the largest error. As the prediction time increases, the error of LSTM becomes large. The ship intelligence platform can provide unified data support and general intelligent algorithms for data-driven applications, and it has the potential to be widely used in coastal environmental monitoring applications.