Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Type Drive Analysis of Urban Water Security Factors

  • Gong, Li (School of Civil Engineering, Lanzhou Jiaotong University) ;
  • Wang, Hong (School of Civil Engineering, Lanzhou Jiaotong University) ;
  • Jin, Chunling (School of Civil Engineering, Lanzhou Jiaotong University) ;
  • Lu, Lili (School of Civil Engineering, Lanzhou Jiaotong University) ;
  • Ma, Menghan (School of Civil Engineering, Lanzhou Jiaotong University)
  • Received : 2020.01.09
  • Accepted : 2020.04.15
  • Published : 2020.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

In order to effectively evaluate the urban water security, the study investigates a novel system to assess factors that impact urban water security and builds an urban water poverty evaluation index system. Based on the contribution rates of Resource, Access, Capacity, Use, and Environment, the study adopts the Water Poverty Index (WPI) model to evaluate the water poverty levels of 14 cities in Gansu during 2011-2018 and uses the least variance method to evaluate water poverty space drive types. The case study results show that the water poverty space drive types of 14 cites fall into four categories. The first category is the dual factor dominant type driven by environment and resources, which includes Lanzhou, Qingyang, Jiuquan, and Jiayuguan. The second category is the three-factor dominant type driven by Access, Use, and Capability, which includes Longnan, Linxia, and Gannan. The third category is the four-factor dominant type driven by Resource, Access, Capability, and Environment, which includes Jinchang, Pingliang, Wuwei, Baiyin, and Zhangye. The fourth category is the five-factor dominant type, which includes Tianshui and Dingxi. The driven types impacting the urban water security factors reflected by the WPI and its model are clear and accurate. The divisions of the urban water security level supply a reliable theoretical and numerical basis for an urban water security early warning mechanism.

Keywords

References

  1. A. Perez-Vidal, J. C. Escobar-Rivera, and P. Torres-Lozada, "Risk assessment in water treatment processes for the development of a Water Safety Plan - WSP," DYNA, vol. 85, no. 206, pp. 304-310, 2018. https://doi.org/10.15446/dyna.v85n206.65427
  2. P. Ginandjar, L. D. Saraswati, D. R. Pangestuti, and S. P. Jati, S. P. (2018). "Implementation of Water Safety Plans (WSPs): a case study in the coastal area in Semarang City, Indonesia," E&ES, vol. 116, no. 1, article no. 012029, 2018.
  3. C. Jin and L. Gong, "On the urban water security assessment based on the pressure-state-response model," Journal of Safety and Environment, vol. 9, no. 5, pp. 104-108, 2009. https://doi.org/10.3969/j.issn.1009-6094.2009.05.026
  4. L. Gong and C. Jin, "Urban water security evaluation system based on water poverty index," Journal of Hydroelectric Engineering, vol. 33, no. 6, pp. 84-90, 2014.
  5. E. G. de Souza, T. M. de Carvalho Studart, M. I. Teixeira Pinheiro, and J. N. Bezerra Campos, "Water safety on Castanhao reservoir, Ceara, Brazil: application of institutional systematization matrix," engenharia sanitaria e ambiental, vol. 22, no. 5, pp. 877-887, 2017. https://doi.org/10.1590/s1413-41522017160289
  6. Y. Wang and D. Chen, "Regional water security evaluation based on krill herd algorithm-maximum entropy projection pursuit model," Northwest Water Resources & Water Engineering, vol. 28, no. 5, pp. 80-86, 2017.
  7. A. Marcantonio, "Water insecurity, illness and other factors of everyday life: a case study from Choma District, Southern Province, Zambia," Water SA, vol. 44, no. 4, pp. 653-663, 2018. https://doi.org/10.4314/wsa.v44i4.14
  8. W. Wang, C. Liu, and C. F. Liu, "Study on evaluation system of urban water supply safety based on multifactor information fusion," Journal of Safety Science and Technology, vol. 14, no. 11, pp. 180-185, 2018.
  9. V. V. Alekseev, I. I. Maksimov, M. V. Semenov, P. V. Mishin, A. G. Terentev, V. I. Medvedev, P. V. Zaytsev, "Application of fuzzy logic elements under the evaluation of water-safety in the plant-soil-air system," Vestnik of the Kazan State Agrarian University, vol. 49, no. 2, pp. 62-66, 2018.
  10. H. Mohammed, I. A. Hameed, and R. Seidu, "Detection of water safety conditions in distribution systems based on artificial neural network and support vector machine," in Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. Cham: Springer, 2019, pp. 567-576.
  11. P. R. Lawrence, J. Meigh, and C. Sullivan, The Water Poverty Index: An International Comparison. Straffordshire, UK: Department of Economics, Keele University, 2002.
  12. J. Shao, Y. Ou, J. Chen, and W. Guo, Water resources security of Yangtze river basin based on water poverty index," Resources and Environment in the Yangtze Basin, vol. 25, no. 6, pp. 889-894, 2016.
  13. J. C. Weaver, "Crop-combination regions in the Middle West," Geographical Review, vol. 44, no. 2, pp. 175-200, 1954. https://doi.org/10.2307/212355
  14. J. Clack, D. A. French, and M. Osorio, "Error analysis of a least squares pseudo-derivative moving least squares method," Proyecciones (Antofagasta), vol. 36, no. 3, pp. 435-448, 2017. https://doi.org/10.4067/S0716-09172017000300435