Volume 6, Issue 2 (Winter 2021)
Health in Emergencies and Disasters Quarterly 2021, 6(2): 123-128 |
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Omidi M R, Jafari Eskandari M, Omidi N. Using Artificial Neural Network to Destroy the Process of Traffic Accident Victims in Yazd Province. Health in Emergencies and Disasters Quarterly 2021; 6 (2) :123-128
URL: http://hdq.uswr.ac.ir/article-1-301-en.html
URL: http://hdq.uswr.ac.ir/article-1-301-en.html
1- Department of Industrial Engineering, Payame Noor University of Tehran, Tehran, Iran. , mromid_91@yahoo.com
2- Department of Industrial Engineering, Payame Noor University of Tehran, Tehran, Iran.
3- Department of Management, Payame Noor University of Tehran, Tehran, Iran.
2- Department of Industrial Engineering, Payame Noor University of Tehran, Tehran, Iran.
3- Department of Management, Payame Noor University of Tehran, Tehran, Iran.
Abstract: (1578 Views)
Background: Road accidents are among the most important causes of death and severe personal and financial injuries. Also, its profound social, cultural, and economic effects threaten human societies. This study aimed to estimate the trend of traffic accident victims in Yazd Province, Iran, to predict the number of traffic accident victims in this province.
Materials and Methods: Based on traffic casualty statistics referred to forensic medicine in Yazd Province within April 1989 and March 2017 referred to Forensic Medicine of Yazd Province and using an artificial neural network to predict the number of injured for 12 months ending in 2020 has been paid. The neural network used in this study had 12 inputs, one output, and 5 hidden layers. The network predicts the relationship between data after training and learning. The network is considered the MSE benchmark.
Results: The number of injured in traffic accidents in Yazd Province in 2020 was equal to 7052 people, with the highest number in December with 832 people and the lowest in June with 414 people. The exact method of use was equal to 92 cases.
Conclusion: The trend of traffic accident casualties in Yazd Province in 2020 will be declining. For future research, the exact method designed in this study can be examined with other methods for the best response level.
Materials and Methods: Based on traffic casualty statistics referred to forensic medicine in Yazd Province within April 1989 and March 2017 referred to Forensic Medicine of Yazd Province and using an artificial neural network to predict the number of injured for 12 months ending in 2020 has been paid. The neural network used in this study had 12 inputs, one output, and 5 hidden layers. The network predicts the relationship between data after training and learning. The network is considered the MSE benchmark.
Results: The number of injured in traffic accidents in Yazd Province in 2020 was equal to 7052 people, with the highest number in December with 832 people and the lowest in June with 414 people. The exact method of use was equal to 92 cases.
Conclusion: The trend of traffic accident casualties in Yazd Province in 2020 will be declining. For future research, the exact method designed in this study can be examined with other methods for the best response level.
Type of Study: Research |
Subject:
General
Received: 2020/04/21 | Accepted: 2020/12/31 | Published: 2021/01/1
Received: 2020/04/21 | Accepted: 2020/12/31 | Published: 2021/01/1
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Health in Emergencies and Disasters Quarterly
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