三峡出闸船舶航速分布规律与预测应用

为监控出闸船舶的异常航速、科学调度出闸船舶、提高船舶出闸效率和保障船舶出闸安全,研究了三峡出闸船舶航速分布规律与预测应用问题.以船舶AIS(automatic identification system)统计数据为基础,通过直方图方法分析了船舶离闸距离、船舶尺度与船舶出闸航速之间的关系,在此基础上,利用80％的AIS数据建立了基于BP(back propagation)神经网络的船舶出闸航速预测模型,并以20％的AIS数据对船舶出闸航速预测模型予以验证,最后通过案例阐释航速预测模型的应用.结果表明:该模型预测航速的平均误差为6.7％,对预测航速和实际航速进行单因素方差分析,两组数据并无显著性差异.可见基于BP神经网络的船舶出闸航速预测模型可以应用于船舶出闸航速预测.

Distribution law and prediction application of ship speed in Three Gorges Project

In order to monitor the abnormal speed of the ship out of the lock, scientifically dispatch the ship out of the lock, and improve the efficiency of the ship out of the lock, the distribution law of the ship speed and the application of the prediction are studied. Based on the AIS statistical data, the relationship between the ship"s distance from the lock, the ship"s size and the ship"s speed is analyzed by histogram method, Based on the BP neural network based prediction model of ship speed is established based on 80% AIS data, and the prediction model is verified with 20% AIS data; Finally, a case was given to illustrate the application of the speed prediction model. It was showed that the average error of the model is 6.7%, and there is no significant difference between the predicted speed and the actual speed by one-way ANOVA. It can be seen that the ship speed prediction model based on BP neural network can be applied to the ship speed prediction.