The aviation safety prediction method based on multivariate gray model
Considering that the causal mechanism of aviation accidents is complicated and has many causal factors with strong grey characteristics,the traditional gray prediction model is only applicable to univariate prediction and has the defect of low prediction accuracy.A method of aviation safety pre-diction was proposed based on a multivariate grey model optimized by genetic algorithm.Firstly,the analysis method of fishbone diagram was applied from the perspective of SHEL model to determine the factors affecting aviation safety,and the correlation coefficient matrix visualization graph was used to further screen the key causative factors.Secondly,a multivariate grey aviation safety prediction model was constructed with human factors,environmental factors,equipment and facility factors,external influencing factors and as the strong input indexes of the prediction model,and the optimal solution of the model's undetermined parameter r was searched globally and parallel by genetic algorithm.Final-ly,simulation experiments were conducted utilizing Chinese civil aircraft accident rate of 10 000 and aviation unsafe event statistics from 2007 to 2016.Predictive comparisons were then made between two gray prediction models,GM(1,1)and MGM(1,n).The findings indicate that compared to the tra-ditional gray model,the proposed method demonstrates an average prediction error of around 1.6%in the aviation safety short-time prediction,showcasing the effectiveness and high accuracy of the pro-posed method.
国家科技期刊平台
NSTL
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