Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
권호 표지
DOI QR코드

DOI QR Code

Land Use Optimization using Genetic Algorithms - Focused on Yangpyeong-eup -

유전 알고리즘을 적용한 토지이용 최적화 배분 연구 - 양평군 양평읍 일대를 대상으로 -

  • Received : 2016.05.23
  • Accepted : 2016.12.28
  • Published : 2017.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Sustainable development is important because the ultimate objective is efficient development combining the economic, social, and environmental aspects of urban conservation. Despite Korea's rapid urbanization and economic development, the distribution of resources is inefficient, and land-use is not an exception. Land use distribution is difficult, as it requires considering a variety of purposes, whose solutions lie in a multipurpose optimization process. In this study, Yangpyeong-eup, Yangpyeong, Gyeonggi-do, is selected, as the site has ecological balance, is well-preserved, and has the potential to support population increases. Further, we have used the genetic algorithm method, as it helps to evolve solutions for complex spatial problems such as planning and distribution of land use. This study applies change to the way of mutation. With four goals and restrictions of area, spatial objectives, minimizing land use conversion, ecological conservation, maximizing economic profit, restricting area to a specific land use, and setting a fixed area, we developed an optimal planning map. No urban areas at the site needed preservation and the high urban area growth rate coincided with the optimization of purpose and maximization of economic profit. When the minimum point of the fitness score is the convergence point, we found optimization occurred approximately at 1500 generations. The results of this study can support planning at Yangpyeong-eup.ausative relationship between the perception of improving odor regulation and odor acceptance.

지속가능한 발전은 도시의 효율적인 개발과 경제, 사회, 환경적 측면의 보전을 목표로 하기 때문에 중요하다. 그러나 우리나라의 빠른 도시화로 경제적 발전은 이루었지만 자원의 비효율적인 배분현상을 경험하게 되었고 이는 토지이용 배분도 예외가 아니다. 토지이용 배분의 문제가 어려운 이유는 다양한 목적을 고려해야하기 때문이며 이는 다목적 최적화의 방법에서 그 해결책을 찾을 수가 있다. 본 연구에서는 생태적으로 보존이 잘 되어있으며 인구 증가가 일어나고 있는 경기도 양평지역의 양평읍과 그 일대를 대상지로 선정하였다. 그리고 넓은 공간 탐색에 유리하고 토지이용 배분의 문제에서 널리 사용되고 있는 유전 알고리즘을 사용하였다. 유전알고리즘(GA)는 더 좋은 자손을 얻기 위하여 염색체의 교차 및 돌연변이의 과정을 거치는 적자생존의 원리가 작용하는 진화의 단계가 그 출발점이다. 본 연구는 변이의 방식에 변화를 주었으며 공간적 목적, 토지이용 전환 최소화, 생태계 보전 최대화, 경제적 이익 최대화라는 네 가지 목적과 특정 토지이용의 면적제한과 고정지역 설정이라는 제약요건을 두고 최적 안을 도출해내었다. 생태적으로 보존시켜야 할 곳에는 시가지가 형성되지 않았고, 시가지 면적 증가율이 높은 결과는 최적화의 방향인 '경제적 이익의 최대화'라는 점과 상응하였다. 적합도 값이 최소인 지점이 수렴지점임을 고려했을 때, 1500세대 부근에서 최적화가 일어났음을 알 수 있었다. 본 연구의 결과는 양평읍과 그 일대에 적용시킬 수 있는 효과적인 지원방안을 마련하는데 도움이 될 수 있을 것으로 판단된다.

Keywords

References

  1. Aerts JCJH, Eisinger E, Heuvelink GBM., Stewart TJ. 2003. Using linear integer programming for multi-site land use allocation. Geographical Analysis. 35: 148-169. https://doi.org/10.1111/j.1538-4632.2003.tb01106.x
  2. Aerts JCJH, Heuvelink GBM. 2002. Using simulated annealing for resource allocation. International Journal of Geographical Information Science. 16(6): 571-587. https://doi.org/10.1080/13658810210138751
  3. Balling RJ, Brown MR, Day K. 1999. Multiobjective urban planning using genetic algorithm. Journal of Urban Planning and Development. 125(2): 86-99. https://doi.org/10.1061/(ASCE)0733-9488(1999)125:2(86)
  4. Cao K, Batty M, Huang B, Liu Y, Yu L, Chen J. 2011. Spatial multi-objective land use optimization: extensions to the nondominated sorting genetic algorithm-II. International Journal of Geographical Information Science. 25(12): 1949-1969. https://doi.org/10.1080/13658816.2011.570269
  5. Cao K, Huang B, Wang S, Lin H. 2012. Sustainable land use optimization using Boundary-based Fast Genetic Algorithm. Computers, Environment and Urban Systems. 36(3): 257-269. https://doi.org/10.1016/j.compenvurbsys.2011.08.001
  6. Choi YG. 2013. The research of finding Cooperation of Land Use and Environment policy for Developing land harmonized with the environment. KRIHS POLICY BRIEF. 425. [Korean Literature]
  7. Chung SS, Jeong SC. 2014. A Study on Urban Land use Drafting Plan Improvement Method through a Comparison Analysis of the Current Residential Area Status and the Demand Forecast Model -With a Focus on a Residential Area Example-. Korea Real Estate Academy. 57: 195-209. [Korean Literature]
  8. Guldmann JM. 1979. Urban land use allocation and environmental pollution control: an intertemporal optimization approach. Socio-Economic Planning Sciences. 13(2): 71-86. https://doi.org/10.1016/0038-0121(79)90041-7
  9. Huang B, Zhang W. 2014. Sustainable land-use planning for a downtown lake area in central China: multiobjective optimization approach aided by urban growth modeling. Journal of Urban Planning and Development. 140(2): 04014002. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000186
  10. Janssen R, Van Herwijnen M, Stewart TJ, Aerts JC. 2008. Multiobjective decision support for land-use planning. Environment and Planning B: Planning and Design. 35(4): 740-756. https://doi.org/10.1068/b33071
  11. Kim EY, Jeon SW, Song WK, Gwack JR, Lee J. 2012. Application of ECVAM as a Indicator for Monitoring National Environment in Korea. 11(2): 3-16. [Korean Literature]
  12. Ligmann-Zielinska A, Church RL, Jankowski P. 2008. Spatial optimization as a generative technique for sustainable multiobjective land-use allocation. International Journal of Geographical Information Science. 22(6): 601-622. https://doi.org/10.1080/13658810701587495
  13. Liu X, Ou J, Li X, Ai B. 2013. Combining system dynamics and hybrid particle swarm optimization for land use allocation. Ecological Modelling. 257: 11-24. https://doi.org/10.1016/j.ecolmodel.2013.02.027
  14. Liu Y, Tang W, He J, Liu Y, Ai T, Liu D. 2015. A land-use spatial optimization model based on genetic optimization and game theory. Computers, Environment and Urban Systems. 49: 1-14. https://doi.org/10.1016/j.compenvurbsys.2014.09.002
  15. Liu Y, Yuan M, He J, Liu Y. 2015. Regional land-use allocation with a spatially explicit genetic algorithm. Landscape and Ecological Engineering. 11(1): 209-219. https://doi.org/10.1007/s11355-014-0267-6
  16. Masnavi MR. 2000. The new millennium and the new urban paradigm: the compact city in practice. Achieving sustainable urban form, 64-73.
  17. Ma X, Zhao X. 2015. Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China. Sustainability. 7(11): 15632-15651. https://doi.org/10.3390/su71115632
  18. Memmah MM, Lescourret F, Yao X, Lavigne C. 2015. Metaheuristics for agricultural land use optimization. A review. Agronomy for Sustainable Development. 35(3): 975-998. https://doi.org/10.1007/s13593-015-0303-4
  19. Moon BR. 2008. Easy Classing Genetic Algorithm. Hanbit Media. Seoul. [Korean Literature]
  20. Morio M, Schadler S, Finkel M. 2013. Applying a multi-criteria genetic algorithm framework for brownfield reuse optimization: Improving redevelopment options based on stakeholder preferences. Journal of Environmental Management. 130: 331-346. https://doi.org/10.1016/j.jenvman.2013.09.002
  21. Park CS. 2005. The Research of Considering Ecological Efficiency of Urban Land Use. [Korean Literature]
  22. Stewart TJ, Janssen R. 2014. A multiobjective GIS-based land use planning algorithm. Computers, Environment and Urban Systems. 46: 25-34. https://doi.org/10.1016/j.compenvurbsys.2014.04.002
  23. Stewart TJ, Janssen R, Van Herwijnen M. 2004. A genetic algorithm approach to multiobjective land use planning. Computers and Operations Research. 31(14): 2293-2313. https://doi.org/10.1016/S0305-0548(03)00188-6
  24. Wang X, Yu S, Huang GH. 2004. Land allocation based on integrated GIS optimization modeling at a watershed level. Landscape and Urban Planning. 66: 61-74. https://doi.org/10.1016/S0169-2046(03)00095-1
  25. Wardoyo W, Jordan GA. 1996. Measuring and assessing management of forested landscapes. The Forestry Chronicle. 72(6): 639-645. https://doi.org/10.5558/tfc72639-6
  26. WCED. 1987. Our common future. Oxford: Oxford University Press.
  27. Yang-pyeonggun. 2016. Hope&Happiness Yangpyung. [Korean Literature]
  28. Zhang W, Huang B. 2014. Land Use Optimization for a Rapidly Urbanizing City with Regard to Local Climate Change: Shenzhen as a Case Study. Journal of Urban Planning and Development. 141(1).

Cited by

  1. 유전알고리즘을 이용한 지속가능 공간최적화 모델 기초연구 - 선행연구 분석을 중심으로 - vol.20, pp.6, 2017, https://doi.org/10.13087/kosert.2017.20.6.133
  2. 옥외공간에서 수목의 다기능을 고려한 최적의 배식 위치 선정 모델 - 수목의 그림자 효과, 시야차단, 개방성을 고려하여 - vol.22, pp.2, 2017, https://doi.org/10.13087/kosert.2019.22.2.1