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.

某海岸环境监测船智能平台样机搭建。开发了LSTM和GBDT方法用于智能平台中的pH值和燃料消耗预测。报告了将通用预测算法应用于实际环境数据和船用柴油机数据的结果。GBDT 具有最好的预测结果和最小的误差。SVM 和 SVR 的预测效果相似，而 FNN 的误差最大。随着预测时间的增加，LSTM 的误差变大。船舶智能平台可以为数据驱动的应用提供统一的数据支持和通用的智能算法，具有广泛应用于沿海环境监测应用的潜力。