Assessment of water resources carrying capacity using chaotic particle swarm genetic algorithm

Yuqin Gao, Li Gao, Yunping Liu, Ming Wu, Zhenxing Zhang

Research output: Contribution to journalArticlepeer-review


Water resources carrying capacity (WRCC) has been evaluated repeatedly to guide sustainable regional development, with the increasing conflicts over water resources between society and nature. Urban underlying surfaces are constantly changing under the rapid development of urbanization, which has changed the WRCC. The chaotic particle swarm genetic algorithm (CPSGA) is proposed in this study to evaluate the WRCC. It combines the genetic algorithm (GA), chaotic optimization algorithm (COA), and particle swarm optimization (PSO), as well as introduces the chaotic mapping of COA and the velocity position update strategy of PSO into the GA framework to strengthen the population quality and improve the algorithm's efficiency. The effectiveness of CPSGA was demonstrated using three typical functions. Nanjing, China, was used as the study area to evaluate the WRCC from 2015 to 2018. The results showed that the comprehensive evaluation scores of the WRCC of Nanjing from 2015 to 2018 were up to 0.83. In addition, the CPSGA had better astringency and stability than GA, COA, and PSO. The application indicated that the proposed methodology is feasible, providing a reference for conducting WRCC research elsewhere.

Original languageEnglish (US)
Pages (from-to)667-686
Number of pages20
JournalJournal of the American Water Resources Association
Issue number2
StatePublished - Apr 2024
Externally publishedYes


  • chaotic particle swarm genetic algorithm
  • evaluation model
  • urbanization
  • water resources carrying capacity

ASJC Scopus subject areas

  • Ecology
  • Water Science and Technology
  • Earth-Surface Processes


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