Seismic vulnerability assessment at urban scale using data mining and GIScience technology: application to Urumqi (China)

Abstract

Seismic vulnerability assessments play a significant role in comprehensive risk mitigation efforts and seismic emergency planning, especially for urban areas with a high population density and a complex construction environment. Traditional approaches such as in situ fieldwork are accurate for conducting seismic vulnerability assessments of buildings; however, they are too much time and cost-consuming, especially in moderate to low seismic hazard regions. To address this issue, an integrated approach for a macroseismic vulnerability assessment composed of data mining methods and GIScience technology was presented and applied to Urumqi, China. First, vulnerability proxies were established via in situ data of buildings in the Tianshan District with an EMS-98 vulnerability classification scheme and two data mining methods, namely, support vector machine and association rule learning methods. Then, vulnerability proxies were applied to the Urumqi database, and the accuracy was validated. Finally, seismic risk maps were constructed through data consisting of direct damage to buildings and human casualties. The results indicated that the two data mining methods could achieve desirable accuracies and stabilities when estimating the seismic vulnerability. The seismic risk of Urumqi was estimated as Slight with a predicted number of 61,380 homeless people for a seismic intensity scenario of VIII.

Keywords:

• seismic vulnerability
• data mining
• GIScience
• EMS-98
• Urumqi

Acknowledgements

We sincerely thank the anonymous reviewers and associated editors for their valuable and constructive comments and suggestions that helped to improve this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant [number 41661134013]; under Grant [number 41601567]; under Grant [number 41601390]; the Key Special Fund for the Study on Rapid Assessment of Multi-source Earthquake Loss under Grant [number 201308018-5].