Abstract
Uncertainty makes the risk evaluation of complex water transportation systems (WTSs) a difficult task. To achieve reasonable results while accounting for uncertainty, the risk evaluation of nautical navigational environments (NNEts) is often based on classical cloud model theory. This study proposes the concept of a risk cloud model (RCM) for NNEt evaluation and uses a fuzzy statistics-based computational approach to obtain the RCM parameters. As a case study, the proposed RCM method was applied to the risk evaluation of the Qiongzhou Strait. The performance of the proposed method was compared to those of a fuzzy theory-based method and an earlier proposed simplified algorithm. The results of the case study demonstrated the effectiveness of the proposed method along with several key advantages. First, the method could deal with uncertainty, take advantage of multichannel information, and evaluate risk features. Second, the RCM droplets intuitively displayed the qualitative and quantitative characteristics of risk levels, which facilitated understanding and analysis. Third, it showed a good sensitivity to ensure the refinement of evaluation results. The proposed method offered an improved approach to NNEt risk evaluation under uncertain conditions.
Highlights
Uncertainty makes the risk evaluation of complex water transportation systems (WTSs) a difficult task
To achieve reasonable results while accounting for uncertainty, the risk evaluation of nautical navigational environments (NNEts) is often based on classical cloud model theory. is study proposes the concept of a risk cloud model (RCM) for NNEt evaluation and uses a fuzzy statistics-based computational approach to obtain the RCM parameters
The proposed RCM method was applied to the risk evaluation of the Qiongzhou Strait. e performance of the proposed method was compared to those of a fuzzy theorybased method and an earlier proposed simplified algorithm. e results of the case study demonstrated the effectiveness of the proposed method along with several key advantages
Summary
A cloud model effectively integrates three variables to represent a qualitative concept in a quantitative manner: the expected value (Ex), entropy (En), and hyperentropy (He) [36]. Is is denoted by C(Ex, En, He), where Ex is the distributed expectation of cloud droplets in the distribution domain, En is the uncertainty (or stochastic) measure of the qualitative concept, and He is the uncertainty measure of the randomness and fuzziness of entropy Based on these initial definitions and numerical characteristics of the cloud model, this study proposes an RCM for NNEt risk evaluation. Computational Approach for Risk Evaluation e computational approach used to obtain the RCM parameters is presented
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