Predicting maritime ship traffic accidents based on historical data mining
Optimize ship navigation routes,reduce delays and congestion caused by traffic accidents,improve maritime transportation efficiency and efficiency,and study a prediction method for maritime ship traffic accidents based on historical data mining.After obtaining historical data of maritime vessel traffic accidents from maritime institutions,the one-dimen-sional local autoencoder in data mining methods is used to mine the historical data of maritime vessel traffic accidents,ob-tain the characteristics of maritime vessel traffic accidents,and then establish the grey SCGM(1,1)C model.The characterist-ics of maritime vessel traffic accidents are inputted into the model,and the intermediate value of the current prediction state is used as a correction.After correcting the prediction results of the grey SCGM(1,1)C model,the prediction results of mari-time vessel traffic accidents are obtained.The experiment shows that this method has strong ability to mine historical data of maritime vessel traffic accidents.The grey SCGM(1,1)C model outputs a high DBI value for predicting maritime vessel traffic accidents,indicating a good ability to predict maritime vessel traffic accidents.
data miningmaritime vesselstraffic accident predictiongrey modelautoencoderpredicted value correction
万方数据
维普
