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IJIET 2013 Vol.3(5): 575-577 ISSN: 2010-3689
DOI: 10.7763/IJIET.2013.V3.338

The Study of Quantitative Forecasting Model on City Emergency Incidents

Nan Gao, Xueming Shu, Jiting Xu, Biao Wen, Peng Chen, and Peng Wu

Abstract—Emergency incidents forecasting is quite significant to emergency response in Mega cities. In this paper, emergency incidents data were collected and data processing and analysis work were conducted. It is obvious that some rules exist when data was counted in different time spans (year and month). With discovered rules regression models were constructed and one of the current popular data mining software, Weka, was used to train and test the models. The results demonstrated that constructed linear regressed year-model and month-models fit the original data well (the MARE, mean average relative absolute error is less than 5%). The year and months' emergency incidents trend can be predicted based on this model. At the end of this article, some factors that produce model deviations were discussed from social activities perspective.

Index Terms—Cross validation, emergency incidents, forecasting, linear regression.

Nan Gao and Jiting Xu are with Computer Science and Engineering Department, University of South Carolina, Columbia, SC 29205 USA (e-mail:gaon@email.sc.edu, xu57@email.sc.edu).
Xueming Shu is with Institute for Public Safety Research, Tsinghua University, Haidian District, Beijing, 100084 (e-mail: shuxm@tsinghua.edu.cn).


Cite:Nan Gao, Xueming Shu, Jiting Xu, Biao Wen, Peng Chen, and Peng Wu, "The Study of Quantitative Forecasting Model on City Emergency Incidents," International Journal of Information and Education Technology vol. 3, no. 5, pp. 575-577, 2013.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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