Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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Optimal tree location model considering multi-function of tree for outdoor space - considering shading effect, shielding, openness of a tree -

옥외공간에서 수목의 다기능을 고려한 최적의 배식 위치 선정 모델 - 수목의 그림자 효과, 시야차단, 개방성을 고려하여 -

  • Park, Chae-Yeon (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Lee, Dong-Kun (Department of Landscape Architecture and Rural system Engineering, Seoul National University) ;
  • Yoon, Eun-Joo (Center for Social and Environmental Systems Research, National Institute for Environment Studies) ;
  • Mo, Yong-Won (Department of Forest Resources and Landscape Architect, College of Life and Applied Sciences, Yeungnam University) ;
  • Yoon, June-Ha (Department of Landscape Architecture and Rural system Engineering, Seoul National University)
  • 박채연 (서울대학교 협동과정조경학) ;
  • 이동근 (서울대학교 조경.지역시스템공학부) ;
  • 윤은주 (일본국립환경연구소 사회환경시스템 연구센터) ;
  • 모용원 (영남대학교 생명응용과학대학 산림자원 및 조경학과) ;
  • 윤준하 (서울대학교 조경.지역시스템공학부)
  • Received : 2018.10.30
  • Accepted : 2019.02.14
  • Published : 2019.04.30
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Abstract

Open space planners and designers should consider scientific and quantified functions of trees when they have to locate where to plant the tree. However, until now, most planners and designers could not consider them because of lack of tool for considering scientific and quantitative tree functions. This study introduces a tree location supporting tool which focuses on the multi-objective including scientific function using ACO (Ant colony optimization). We choose shading effect (scientific function), shielding, and openness as objectives for test application. The results show that when the user give a high weight to a particular objective, they can obtain the optimal results with high value of that objective. When we allocate higher weight for the shading effect, the tree plans provide larger shadow value. Even when compared with current tree plan, the study result has a larger shading effect plan. This result will reduce incident radiation to the ground and make thermal friendly open space in the summer. If planners and designers utilize this tool and control the objectives, they would get diverse optimal tree plans and it will allow them to make use of the many environmental benefits from trees.

Keywords

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Figure 1. Research Site for pilot test. (left: aerial photograph, right: model domain)

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Figure 2. Score map for shielding and openness tree functions. (score: 0∼20)

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Figure 3. Intercepted solar radiation calculation for shading. (up: building, down: tree)

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Figure 4. ACO algorithm for optimal tree location model

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Figure 5. Tree location results from the different weights simulations

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Figure 6. The variation of objective Function value(F). (x axis: iteration, y axis: F).

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Figure 7. Tree location result (scenario 3, left) and real tree location (right).

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Figure 8. shade map (building+tree) of model result (scenario 3, left) and real tree location (right)

Table 2. Input parameters

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Table 3. Functions’ weight in the three different scenarios

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Table 4. Fbest, shadow value in each scenario

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References

  1. Aerts, J. C. J. H..Heuvelink, G. B. M. 2002. Using simulated annealing for resource allocation. International Journal of Geographical Information Science, 16(6), 571-587. https://doi.org/10.1080/13658810210138751
  2. Brookes, C. J. 2001. A genetic algorithm for designing optimal patch configurations in GIS. International Journal of Geographical Information Science. 15(6), 539-559. https://doi.org/10.1080/136588101316907227
  3. Gromke, C..Blocken, B. 2015. Influence of avenue-trees on air quality at the urban neighborhood scale. Part II: Traffic pollutant concentrations at pedestrian level. Environmental Pollution, 196, 176-184. https://doi.org/10.1016/j.envpol.2014.10.015
  4. Holst, J.,.Mayer, H. 2011. Impacts of street design parameters on human-biometeorological variables. Meteorologische Zeitschrift, 20(5), 541-552. https://doi.org/10.1127/0941-2948/2011/0254
  5. Huang, Y. J..Akbari, H..Taha, H..Rosenfeld, A. H. 1987. The Potential of Vegetation in Reducing Summer Cooling Loads in Residential Buildings. American Meteorological Society, 26, 1103-1116.
  6. Lee, H..Holst, J..Mayer, H. 2013. Modification of human-biometeorologically significant radiant flux densities by shading as local method to mitigate heat stress in summer within urban street canyons. Advances in Meteorology, 2013. https://doi.org/10.1155/2013/312572
  7. Li, X..He, J..Liu, X. 2009. Intelligent GIS for solving high-dimensional site selection problems using ant colony optimization techniques. International Journal of Geographical Information Science, 23(4), 399-416. https://doi.org/10.1080/13658810801918491
  8. Li, X..Parrott, L. 2016. An improved Genetic Algorithm for spatial optimization of multiobjective and multi-site land use allocation. Computers, Environment and Urban Systems, 59, 184-194. https://doi.org/10.1016/j.compenvurbsys.2016.07.002
  9. Liu, X..Li, X..Shi, X..Huang, K..Liu, Y. 2012. A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas. International Journal of Geographical Information Science, 26(7), 1325-1343. https://doi.org/10.1080/13658816.2011.635594
  10. Liu, Y..Tang, D..Liu, D..Kong, X. 2012. A Land Use Spatial Allocation Model based on Ant Colony Optimization. Geog.Leeds. Ac.Uk, 8(1991), 1115-1126.
  11. Lopez, B. E..Urban, D..Whiter, P. S. 2018. Nativity and seed dispersal mode influence species' responses to habitat connectivity and urban environments. Global Ecology and Biogeography, 1-14. https://doi.org/10.1111/geb.12760
  12. Ma, S..Li, X..Cai, Y. 2017. Delimiting the urban growth boundaries with a modified ant colony optimization model. Computers, En vironment a nd Urba n Systems, 62, 146-155. https://doi.org/10.1016/j.compenvurbsys.2016.11.004
  13. Maniezzo, A. C. M. D. V. 1992. Distributed optimization by ant colonies. In Toward a practice of autonomous systems: proceedings of the First European Conference on Artificial Life. Mit Press.
  14. Park, C. Y..Lee, D. K..Krayenhoff, E. S.. Heo, H.K..Ahn , S..Asawa, T.. Akinobu, M..Kim, H. G. 2018. A multilayer mean radiant temperature model for pedestrians in a street canyon with trees. Building and Environment, 141, 298-309. https://doi.org/10.1016/j.buildenv.2018.05.058
  15. Park, Y..Lee, D..Yoon, E..Mo, Y..Leem, J. 2017. Land Use Optimization using Genetic Algorithms. Journal of Environmental Impact Assessment, 26(1), 44-56. (in Korean with English summary) https://doi.org/10.14249/eia.2017.26.1.44
  16. Yoon, E. J..Lee, D. K. 2017. Basic Study on Spatial Optimization Model for Sustainability using Genetic Algorithm -Based on Literature Review-. Journal of Korean Environmental Restoration Technology, 20(6), 133-149.
  17. Yoon, E. J..Song, E. J..Jeung, Y. H..Kim, E. Y..Lee, D. K. 2018. Spatial Decision Support System for Development and Conservation of Unexecuted Urban Park using ACO. Journal of Korean Environmental Restoration Technology, 21(2), 39-51. (in Korean with English summary)
  18. Yoon, E. J..Kim, B.Lee, D. K. 2017. 2019. Multi-objective planning model for urban greening based on optimization algorithms. Urban Forestry & Urban Greening, In press. https://doi.org/10.1016/j.ufug.2019.01.004