A density prediction method for fishing vessel based on deep learning and fusion of spatial-temporal features
To mine hidden information from massive AIS trajectory data and provide a scientific basis for decision-making of marine fishery management departments,this paper proposes a marine fishing vessel density prediction method based on deep learning and fusion of spatial-temporal features.Firstly,the driving area of fishing vessels is grided according to fishing vessel trajectory dataset.Secondly,high-density fishing vessel areas are selected for study to avoid data sparsity.Thirdly,the fishing vessel distribution data is constructed into a three-dimensional ma-trix of spatial and temporal fusion.Finally,the convolutional recurrent neural network model is used to capture spa-tial and temporal features,while the convolutional neural network is stacked to enhance the learning of spatial fea-tures.The experiment was specifically tested with real fishing vessel trajectory data of the East China Sea.Results showed that the predicted values of fishing vessel density were very close to the true values,with an average abso-lute error of 4x10-4.It indicates that the model can better fit the distribution characteristics of fishing vessel densi-ty,which improve effectively the accuracy and robustness of fishing hotspot prediction.
fisheries resourcesfishing vessel density predictiondeep learningconvolutional recurrent neural network
万方数据
维普
