Environmental Science and Pollution Research

, Volume 21, Issue 22, pp 13027–13042 | Cite as

Modeling urban growth by the use of a multiobjective optimization approach: Environmental and economic issues for the Yangtze watershed, China

  • Wenting Zhang
  • Haijun Wang
  • Fengxiang Han
  • Juan Gao
  • Thuminh Nguyen
  • Yarong Chen
  • Bo Huang
  • F. Benjamin Zhan
  • Lequn Zhou
  • Song HongEmail author
Research Article


Urban growth is an unavoidable process caused by economic development and population growth. Traditional urban growth models represent the future urban growth pattern by repeating the historical urban growth regulations, which can lead to a lot of environmental problems. The Yangtze watershed is the largest and the most prosperous economic area in China, and it has been suffering from rapid urban growth from the 1970s. With the built-up area increasing from 23,238 to 31,054 km2 during the period from 1980 to 2005, the watershed has suffered from serious nonpoint source (NPS) pollution problems, which have been mainly caused by the rapid urban growth. To protect the environment and at the same time maintain the economic development, a multiobjective optimization (MOP) is proposed to tradeoff the multiple objectives during the urban growth process of the Yangtze watershed. In particular, the four objectives of minimization of NPS pollution, maximization of GDP value, minimization of the spatial incompatibility between the land uses, and minimization of the cost of land-use change are considered by the MOP approach. Conventionally, a genetic algorithm (GA) is employed to search the Pareto solution set. In our MOP approach, a two-dimensional GA, rather than the traditional one-dimensional GA, is employed to assist with the search for the spatial optimization solution, where the land-use cells in the two-dimensional space act as genes in the GA. Furthermore, to confirm the superiority of the MOP approach over the traditional prediction approaches, a widely used urban growth prediction model, cellular automata (CA), is also carried out to allow a comparison with the Pareto solution of MOP. The results indicate that the MOP approach can make a tradeoff between the multiple objectives and can achieve an optimal urban growth pattern for Yangtze watershed, while the CA prediction model just represents the historical urban growth pattern as the future growth pattern. Moreover, according to the spatial clustering index, the urban growth pattern predicted through MOP is more reasonable. In summary, the proposed model provides a set of Pareto urban growth solutions, which compromise environmental and economic issues for the Yangtze watershed.


Yangtze watershed Multiobjective optimization (MOP) Genetic algorithm (GA) Cellular automata (CA) Non-point source (NPS) 



This study was supported by funding from the CHINA SCHOLARSHIP COUNCIL (File No. 201308420279), National Natural Science Foundation of China (NSFC: 40871179) and Hong Kong Research Grants Council (RGC: 444612). The authors are also very grateful to the Data Sharing Infrastructure of Earth System Science and Changjiang Soil and Water Conservation Monitoring Centre, CWRC, China for providing the land-use data of the Yangtze watershed between 1980 and 2005.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Wenting Zhang
    • 1
  • Haijun Wang
    • 2
  • Fengxiang Han
    • 3
  • Juan Gao
    • 2
  • Thuminh Nguyen
    • 2
  • Yarong Chen
    • 2
  • Bo Huang
    • 1
  • F. Benjamin Zhan
    • 4
  • Lequn Zhou
    • 3
  • Song Hong
    • 2
    • 4
    Email author
  1. 1.Department of Geography and Resource ManagementThe Chinese University of Hong KongHong KongChina
  2. 2.School of Resource and Environmental ScienceWuhan UniversityWuhanChina
  3. 3.Changjiang Soil and Water Conservation Monitoring Centre, CWRCWuhanChina
  4. 4.Department of GeographyTexas State UniversitySan MarcosUSA

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