Prediction of Construction and Production Safety Accidents in China Based on Time Series Analysis Combination Model
Source abstract
Abstract
At present, the data related to construction safety accidents in China have the characteristics of small sample size, large fluctuation, large base, nonlinearity, etc. It is of great significance in accident prevention to break through the limitations of traditional prediction models in dealing with time series and reduce the contingency of prediction results. The purpose of this study was to use the trend decomposition method to reduce the fluctuation of non-stationary time series and use the combination of an autoregressive integrated moving average model and Grey model with fractional order accumulation to accurately predict construction accidents. This paper analyzed the number of production safety accidents and deaths in housing municipal engineering in China from 2009 to 2019, which makes monthly, quarterly and annual forecasts and compares forecast results. The FOAGM model is based on a genetic algorithm and can forecast individual months separately, which improves the overall forecast of accident numbers. The rolling forecast was used to provide an idea of hierarchical optimization of the forecast results for the annual accident number forecast with a small number of samples. The study also emphasized that the prediction of the death toll is affected by larger and above accidents. By using the CRITIC method, the impacts were quantified, so as to revise the prediction results.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Grey System Theory Applications · Energy Load and Power Forecasting · Occupational Health and Safety Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil and Resource Engineering, University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing 100083, China
Funding
National Key R&D Program of China
References (32)
Show source-provided references
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