Research and Application of Improved Multiple Imputation Based on R Language in Fire Prediction

Fire

article

Jie Wang¹  Meilin Yang¹  Tianming Li¹  Xuepeng Jiang¹  Kaihua Lu⁴ 

[1] School of Resource and Environmental Engineering, Wuhan University of Science and Technology;Hubei Research Center of Industrial Safety Engineering Technology;Safety and Emergency Response Institute, Wuhan University of Science and Technology;Faculty of Engineering, China University of Geosciences

关键词: R language;    mice package;    HAZOP;    Bayesian network;    fire trend;   

DOI  :  10.3390/fire6060235

学科分类：环境科学（综合）

来源: mdpi

【 摘 要 】

An improved multiple imputation based on R language is proposed to deal with the miss of data in a fire prediction model, which can affect the accuracy of the prediction results. Hazard and operability (HAZOP) is used to accurately find the data related to the research purpose, and exclude data with a missing rate greater than 80% and small differences in characteristics. Then, by changing the m value in the mice package under the R language (R-mice), the relevant parameters of the complete filling factor set under different m values are obtained. The value of m is determined after observing and comparing the parameters. The proposed method fully considers the randomness of filling and the difference between the generated dataset. Taking Hubei Province as an example, the data processed by this method are used as the input of the Bayesian network, and the fire trend is used as the output. The results show that the improved multiple imputation based on R-mice can solve the problem of missing data very well, and have a high prediction effect (AUC = 94.0800). In addition, the results of the predictive reasoning and sensitivity analysis show that the government’s supervision has a vital influence on the trend of fires in Hubei Province.

【 授权许可】

CC BY