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Dynamic urban land-use change management using multi-objective evolutionary algorithms

  • Zohreh MasoumiEmail author
  • Carlos A. Coello Coello
  • Ali Mansourian
Methodologies and Application
  • 1 Downloads

Abstract

Frequent land-use changes in urban areas require an efficient and dynamic approach to reform and update detailed plans by re-arrangement of surrounding land-uses in case of change in one or several urban land-uses. However, re-arrangement of land-uses is problematic, since a variety of conflicting criteria must be considered and satisfied. This paper proposes and examines a two-step approach to resolve the issue. The first step adopts a multi-objective optimization technique to obtain an optimal arrangement of surrounding land-uses in case of change in one or several urban land-uses, whereas the second step uses clustering analysis to produce appropriate solutions for decision makers from the outputs of the first step. To present and assess the approach, a case study was conducted in Tehran, the capital of Iran. To satisfy the first step, four conflicting objective functions including maximization of consistency, maximization of dependency, maximization of suitability and maximization of compactness were defined and optimized using non-dominated sorting genetic algorithm. Per-capita demand was also employed as a constraint in the optimization process. Clustering analysis based on ant colony optimization was used to satisfy the second step. The results of the optimization were satisfactory both from a convergence and from a repeatability point of view. Furthermore, the objective functions of optimized arrangements were better than existing land-use arrangement in the area, with the per-capita demand deficiency significantly compensated. The approach was also communicated to urban planners in order to assess its usefulness. In conclusion, the proposed approach can extensively support and facilitate decision making of urban planners and policy makers in reforming and updating existing detailed plans after land-use changes.

Keywords

Dynamic urban land-use change Multi-objective optimization NSGA-II Clustering Decision support Soft computing 

Notes

Acknowledgements

C.A. Coello Coello gratefully acknowledges support from CONACyT grant no. 2016-01-1920 and from a project from the 2018 SEP-Cinvestav Fund (application no. 4).

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Earth SciencesInstitute for Advanced Studies in Basic SciencesZanjanIran
  2. 2.Center for Research in Climate Change and Global Warming (CRCC)ZanjanIran
  3. 3.Department of ComputationCINVESTAV-IPN (Evolutionary Computation Group)Mexico CityMexico
  4. 4.Department of Physical Geography and Ecosystem ScienceLund UniversityLundSweden
  5. 5.Center for Middle Eastern Studies, MECWLund UniversityLundSweden

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