Abstract
To deal with highly time complexity and unstable assessments for conflicting evidences from various navigation factors, we put forward an innovative assessment scheme of navigation risk based on the improved multi-source information fusion techniques. Different from the existing studies, we first deduce the nonlinear support vector machine classification model for the general scenario. The slack variable is adaptively computed based on the Euclidean distance ratio. Considering the unsatisfactory characteristics of the standard Dempster–Shafer evidence theory, the optimal combination rule is derived step by step. What"s more, the lowly dimensional Kalman filter is applied to forecast the navigation risk. Simultaneously, the time complexity of each technique is analyzed. With respect to the vessel navigation risk, the assessment results are provided to indicate the reliability and efficiency of the proposed scheme.
Highlights
Navigation risk defines the occurrence likelihood of traffic accidents
We find that few works have been reported on the vessel navigation risk assessment by the nonlinear support vector machine (SVM) classification model and optimal D-S evidence theory
The remainder of this note is organized as follows: First, the current issues of the vessel navigation risk assessment are formulated in section ‘‘Preliminaries.’’ In section ‘‘Methodology,’’ the nonlinear edition of the SVM classification model is derived by exploring the slack variable
Summary
Navigation risk defines the occurrence likelihood of traffic accidents. As we know, the navigation factor mainly includes three kinds of important sub-factors, that is, hydrological sub-factor, environmental sub-factor, and navigable sub-factor. We find that few works have been reported on the vessel navigation risk assessment by the nonlinear SVM classification model and optimal D-S evidence theory. We further extend the proposed scheme to forecast the future risk, which saves time complexity of the back propagation neural network (BPNN) in Liu and colleagues.[23,24,25] Take the case of the vessel navigation risk, we analyze how to integrate different techniques and improve both efficiency and reliability in order to accomplish risk assessment in the complex waters. The remainder of this note is organized as follows: First, the current issues of the vessel navigation risk assessment are formulated in section ‘‘Preliminaries.’’ In section ‘‘Methodology,’’ the nonlinear edition of the SVM classification model is derived by exploring the slack variable. Aimed at the independent of three kinds of the mentioned sub-factors, the reliability of heterogeneous sensors should be taken into consideration
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