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Agent-based modeling and simulations of land-use and land-cover change according to ant colony optimization: a case study of the Erhai Lake Basin, China

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  • Xu QuanLi
  • Yang Kun
  • Wang GuiLin
  • Yang YuLian

Abstract

The land-use structure and ecological service functions of the Erhai Lake Watershed are being altered by rapid socioeconomic development and urbanization, which will ultimately lead to the generation and aggravation of agricultural and urban non-point source pollution over the entire region. Therefore, the relationships between human activities and land-use/land-cover changes (LUCCs) must be studied to support scientific decisions regarding reasonable land planning and land use. This paper combines geographic information system technology for spatial analysis and the ant colony optimization artificial intelligence algorithm. Moreover, this study applies agent-based modeling to establish a spatiotemporal process model for LUCCs that effectively simulates the dynamic land-use changes in the basin. A selection is first made and evaluated for dynamic land-use change impact factors. Then, the agent classes and their rules in the LUCC processes are established. The program is designed using the Java programming language, and the model is implemented based on the Repast modeling platform. Finally, the models are validated, and the simulated results are analyzed and discussed. Some conclusions were drawn from the experiments, as well as some policies on land use were suggested. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Xu QuanLi & Yang Kun & Wang GuiLin & Yang YuLian, 2015. "Agent-based modeling and simulations of land-use and land-cover change according to ant colony optimization: a case study of the Erhai Lake Basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 95-118, January.
    • Handle: RePEc:spr:nathaz:v:75:y:2015:i:1:p:95-118
    DOI: 10.1007/s11069-014-1303-4
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    References listed on IDEAS

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    1. Nina Schwarz & Daniel Kahlenberg & Dagmar Haase & Ralf Seppelt, 2012. "ABMland - a Tool for Agent-Based Model Development on Urban Land Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-8.
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    Cited by:

    1. Opelele Omeno Michel & Yu Ying & Fan Wenyi & Chen Chen & Kachaka Sudi Kaiko, 2021. "Examining Land Use/Land Cover Change and Its Prediction Based on a Multilayer Perceptron Markov Approach in the Luki Biosphere Reserve, Democratic Republic of Congo," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    2. Huafei Yu & Yaolong Zhao & Yingchun Fu, 2019. "Optimization of Impervious Surface Space Layout for Prevention of Urban Rainstorm Waterlogging: A Case Study of Guangzhou, China," IJERPH, MDPI, vol. 16(19), pages 1-28, September.
    3. Ghazali, Mahboubeh & Honar, Tooraj & Nikoo, Mohammad Reza, 2018. "A hybrid TOPSIS-agent-based framework for reducing the water demand requested by stakeholders with considering the agents’ characteristics and optimization of cropping pattern," Agricultural Water Management, Elsevier, vol. 199(C), pages 71-85.
    4. Bhanage Vinayak & Han Soo Lee & Shirishkumar Gedem, 2021. "Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model," Sustainability, MDPI, vol. 13(2), pages 1-22, January.

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