[1]

Zhang S, Wei L, Wang R, Duo Y. 2021. Analysis and research on chemical and hazardous chemicals accidents in China during 2016—2020. Journal of Safety Science and Technology 17:119−26

doi: 10.11731/j.issn.1673-193x.2021.10.018
[2]

Subramanian V, Peijnenburg WJGM, Vijver MG, Blanco CF, Cucurachi S, Guinée JB. 2023. Approaches to implement safe by design in early product design through combining risk assessment and Life Cycle Assessment. Chemosphere 311:137080

doi: 10.1016/j.chemosphere.2022.137080
[3]

Wan J, Rong Z, Zhao Y, Li Y. 2021. Statistical Analysis and Lessons of Major Production Safety Accidents in Chemical Industry of China from 2010 to 2019. Industrial Safety and Environmental Protection 47:59−63

doi: 10.3969/j.issn.1001-425X.2021.05.015
[4]

Luo X, Feng X, Ji X, Dang Y, Zhou L, et al. 2023. Extraction and analysis of risk factors from Chinese chemical accident reports. Chinese Journal of Chemical Engineering 61:68−81

doi: 10.1016/j.cjche.2023.02.026
[5]

Pan X, Zhong B, Wang Y, Shen L. 2022. Identification of accident-injury type and bodypart factors from construction accident reports: A graph-based deep learning framework. Advanced Engineering Informatics 54:101752

doi: 10.1016/j.aei.2022.101752
[6]

Wang B, Li D, Wu C. 2020. Characteristics of hazardous chemical accidents during hot season in China from 1989 to 2019: A statistical investigation. Safety Science 129:104788

doi: 10.1016/j.ssci.2020.104788
[7]

Hua W, Chen J, Qin Q, Wan Z, Song L. 2021. Causation analysis and governance strategy for hazardous cargo accidents at ports: Case study of Tianjin Port's hazardous cargo explosion accident. Marine Pollution Bulletin 173:113053

doi: 10.1016/j.marpolbul.2021.113053
[8]

Ye Y, Xia X, Li Z. 2012. Statistical Analysis of Typical Chemical Industry Accidents. Industrial Safety and Environmental Protection 38(8):49−51,55

doi: 10.3969/j.issn.1001-425X.2012.09.016
[9]

Zhu Z, Lin Z, Chen L, Dong H, Gao Y, et al. 2023. Correlation knowledge extraction based on data mining for distribution network planning. Global Energy Interconnection 6:485−92

doi: 10.1016/j.gloei.2023.08.008
[10]

Castro Y, Kim YJ. 2015. Data mining on road safety: factor assessment on vehicle accidents using classification models. International Journal of Crashworthiness 21:104−11

doi: 10.1080/13588265.2015.1122278
[11]

Kim J, Ryu KR. 2015. Mining traffic accident data by subgroup discovery using combinatorial targets. 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), 17-20 November 2015, Marrakech, Morocco. USA: IEEE. pp. 1-6. https://doi.org/10.1109/AICCSA.2015.7507171

[12]

Al Najada H, Mahgoub I. 2016. Big vehicular traffic Data mining: Towards accident and congestion prevention. 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), 5-9 September 2016, Paphos, Cyprus. USA: IEEE. pp. 256-61. https://doi.org/10.1109/IWCMC.2016.7577067

[13]

Niu Y, Fan Y, Gao Y. 2019. Topic extraction on causes of chemical production accidents based on data mining. Journal of Safety Science and Technology 15:165−70

doi: 10.11731/j.issn.1673-193x.2019.10.026
[14]

Yang JF, Wang PC, Liu XY, Bian MC, Chen LC, et al. 2023. Analysis on causes of chemical industry accident from 2015 to 2020 in Chinese mainland: A complex network theory approach. Journal of Loss Prevention in the Process Industries 83:105061

doi: 10.1016/j.jlp.2023.105061
[15]

Wu Y, Fu G, Wu Z, Wang Y, Xie X, et al. 2023. A popular systemic accident model in China: Theory and applications of 24Model. Safety Science 159:106013

doi: 10.1016/j.ssci.2022.106013
[16]

Qiu Z, Liu Q, Li X, Zhang J, Zhang Y. 2021. Construction and analysis of a coal mine accident causation network based on text mining. Process Safety and Environmental Protection 153:320−28

doi: 10.1016/j.psep.2021.07.032
[17]

Kang Y, Zhao R, Chen W, Jiao Y, Han W. 2023. Construction and empirical study of MMEM-SV assessment model of safety culture in marine engineering enterprises. Safety and Environmental Engineering 30:21−27

doi: 10.13578/j.cnki.issn.1671-1556.20220120
[18]

Zhang Y, Xiong Z, Geng X, Chen J. 2011. Analysis and Improvement of Eclat Algorithm. computer Engineering 36:28−30

doi: 10.3969/j.issn.1000-3428.2010.23.010
[19]

Zhao Y, Zhang H, Tong C. 2019. Gas disaster early warning model based on Eclat algorithm. Journal of Heilongjiang University Science& Technology 29:515−20

doi: 10.3969/j.issn.2095-7262.2019.04.024
[20]

Song K, Lee K. 2017. Predictability-based collective class association rule mining. Expert Systems with Applications 79:1−7

doi: 10.1016/j.eswa.2017.02.024
[21]

Wang L, Guo Y, Guo Y, Xia X, Zhang Z, et al. 2023. An improved eclat algorithm based association rules mining method for failure status information and remanufacturing machining schemes of retired products. Procedia CIRP 118:572−77

doi: 10.1016/j.procir.2023.06.098
[22]

Liu Z, He S. 2023. Association rule mining for causes of railway traffic accidents based on improved apriori algorithm. Railway Transport and Economy 45:120−126,140

doi: 10.16668/j.cnki.issn.1003-1421.2023.04.17
[23]

Rafindadi AD, Shafiq N, Othman I, Ibrahim A, Aliyu MM, et al. 2023. Data mining of the essential causes of different types of fatal construction accidents. Heliyon 9:e13389

doi: 10.1016/j.heliyon.2023.e13389
[24]

Zhang R, Lowndes IS. 2010. The application of a coupled artificial neural network and fault tree analysis model to predict coal and gas outbursts. International Journal of Coal Geology 84:141−52

doi: 10.1016/j.coal.2010.09.004
[25]

Wu Y, Fu G, Han M, Jia Q, Lyu Q, et al. 2022. Comparison of the theoretical elements and application characteristics of STAMP, FRAM, and 24Model: A major hazardous chemical explosion accident. Journal of Loss Prevention in the Process Industries 80:104880

doi: 10.1016/j.jlp.2022.104880
[26]

Li L, Zhang Y, Li X. 2022. Network analysis on causes for chemical accidents based on text mining. Journal of Wuhan University of Technology 44:637−643,655

doi: 10.3963/j.issn.2095-3852.2022.04.019
[27]

Luan C. 2013. The application of network centrality index in technical measurement. Science & Technology Progress and Policy 30:10−13

doi: 10.6049/kjjbydc.2012010129
[28]

Li Y, Wu K, Liu J. 2023. Self-paced ARIMA for robust time series prediction. Knowledge-Based Systems 269:110489

doi: 10.1016/j.knosys.2023.110489
[29]

ArunKumar KE, Kalaga DV, Mohan Sai Kumar C, Kawaji M, et al. 2022. Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alexandria Engineering Journal 61:7585−603

doi: 10.1016/j.aej.2022.01.011
[30]

ArunKumar KE, Kalaga DV, Sai Kumar CM, Chilkoor G, Kawaji M, et al. 2021. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Applied Soft Computing 103:107161

doi: 10.1016/j.asoc.2021.107161
[31]

Braz MS, Sáfadi T, Ferreira RA, Morais MHF, Silva Z, et al. 2023. Temporal relationship between human and canine visceral leishmaniasis in an urban area in southeastern Brazil: An application of the ARIMAX model. Preventive Veterinary Medicine 215:105921

doi: 10.1016/j.prevetmed.2023.105921
[32]

Hossain MS, Ahmed S, Uddin MJ. 2021. Impact of weather on COVID-19 transmission in south Asian countries: An application of the ARIMAX model. Science of The Total Environment 761:143315

doi: 10.1016/j.scitotenv.2020.143315
[33]

Dey B, Roy B, Datta S, Ustun TS. 2023. Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models. Energy Reports 9:411−18

doi: 10.1016/j.egyr.2022.11.038
[34]

Jiang S, Yang C, Guo J, Ding Z. 2018. ARIMA forecasting of China's coal consumption, price and investment by 2030. Energy Sources, Part B: Economics, Planning, and Policy 13:190−95

doi: 10.1080/15567249.2017.1423413
[35]

Zhao L, Li Z, Qu L. 2022. Forecasting of Beijing PM2.5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition. Heliyon 8:e12239

doi: 10.1016/j.heliyon.2022.e12239