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Comparison of three hybrid models to simulate land use changes: a case study in Qeshm Island, Iran

Authors
  • Kourosh Niya, Ali1
  • Huang, Jinliang1
  • Kazemzadeh-Zow, Ali2
  • Karimi, Hazhir3
  • Keshtkar, Hamidreza4
  • Naimi, Babak5
  • 1 Xiamen University, Xiamen, Fujian, 361102, China , Xiamen (China)
  • 2 University of Tehran, Tehran, Iran , Tehran (Iran)
  • 3 University of Zakho, Zakho, Kurdistan Region, Iraq , Zakho (Iraq)
  • 4 University of Tehran, Karaj, Iran , Karaj (Iran)
  • 5 University of Helsinki, Helsinki, 00014, Finland , Helsinki (Finland)
Type
Published Article
Journal
Environmental Monitoring and Assessment
Publisher
Springer-Verlag
Publication Date
Apr 22, 2020
Volume
192
Issue
5
Identifiers
DOI: 10.1007/s10661-020-08274-6
Source
Springer Nature
Keywords
License
Yellow

Abstract

Land use change simulation is an important issue for its role in predicting future trends and providing implications for sustainable land management. Hybrid models have become a recognized strategy to inform decision-makers, but further attempts are needed to warrant the reliability of their projected results. In view of this, three hybrid models, including the cellular automata-Markov chain-artificial neural network, cellular automata-Markov chain-logistic regression, and Markov chain-artificial neural network, were applied to simulate land use change on the largest island in Iran, Qeshm Island. The Figure of Merit (FOM) was used to measure the modeling accuracy of the simulations, with the FOMs for the three models 6.7, 5.1, and 4.5, respectively. Consequently, the cellular automata-Markov chain-artificial neural network most precisely simulates land use change on Qeshm Island and is, thus, used to simulate land use change until 2026. The simulation shows that the incremental trend of the built-up class will continue in the coming years. Meanwhile, the areas of valuable ecosystems, such as mangroves, tend to decrease. Despite the protection plans for mangroves, these areas require more attention and conservation planning. This study demonstrates a referential example to select the proper land use models for informing planning and management in similar coastal zones.

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