A copula‐based method of risk prediction for autonomous underwater gliders in dynamic environments

Abstract

Autonomous underwater gliders (AUGs) are effective platforms for oceanic research and environmental monitoring. However, complex underwater environments with uncertainties could pose the risk of vehicle loss during their missions. It is therefore essential to conduct risk prediction to assist decision making for safer operations. The main limitation of current studies for AUGs is the lack of a tailored method for risk analysis considering both dynamic environments and potential functional failures of the vehicle. Hence, this study proposed a copula‐based approach for evaluating the risk of AUG loss in dynamic underwater environments. The developed copula Bayesian network (CBN) integrated copula functions into a traditional Bayesian belief network (BBN), aiming to handle nonlinear dependencies among environmental variables and inherent technical failures. Specifically, potential risk factors with causal effects were captured using the BBN. A Gaussian copula was then employed to measure correlated dependencies among identified risk factors. Furthermore, the dependence analysis and CBN inference were performed to assess the risk level of vehicle loss given various environmental observations. The effectiveness of the proposed method was demonstrated in a case study, which considered deploying a Slocum G1 Glider in a real water region. Risk mitigation measures were provided based on key findings. This study potentially contributes a tailored tool of risk prediction for AUGs in dynamic environments, which can enhance the safety performance of AUGs and assist in risk mitigation for decision makers.

Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model

“…Also, they belong to the decision-making problem of multi-objective attributes, and the decision-making information is ambiguous and complex. The assessment methods for such problems mainly include Analytic Hierarchy Process (AHP) [13], Analytical Network Process (ANP) [14], Fuzzy Comprehensive Evaluation Method (FCEM) [15], FMEA [16], Bayesian Network (BN) [17,18], and Truoket theory [19]. Among them, AHP is a classic multi-objective decision-making method, which is widely used in the evaluation of flood disasters [13], hazardous materials transportation [20], the cement industry [21], and other aspects of evaluation, which illustrate the effectiveness of this method.…”