An Uncertain Programming Model for Land Use Structure Optimization to Promote Effectiveness of Land Use Planning

LI Xin; MA Xiaodong

doi:10.1007/s11769-017-0896-4

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LI Xin, MA Xiaodong. An Uncertain Programming Model for Land Use Structure Optimization to Promote Effectiveness of Land Use Planning[J]. Chinese Geographical Science, 2017, 27(6): 974-988. doi: 10.1007/s11769-017-0896-4

Citation:

LI Xin, MA Xiaodong. An Uncertain Programming Model for Land Use Structure Optimization to Promote Effectiveness of Land Use Planning[J]. Chinese Geographical Science, 2017, 27(6): 974-988. doi: 10.1007/s11769-017-0896-4

An Uncertain Programming Model for Land Use Structure Optimization to Promote Effectiveness of Land Use Planning

doi: 10.1007/s11769-017-0896-4

LI Xin^1,2,
MA Xiaodong^1
,

1.
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China;
2.
Department of Earth and Environmental Sciences, University of British Columbia, Okanagan Campus, Kelowna V1V1V7, Canada

Funds: Under the auspices of National Natural Science Foundation of China (No. 41401627, 41471144), Foundation Research Project of Jiangsu Province (No. BK20140236)

More Information

Corresponding author: MA Xiaodong.E-mail:xiaodgma@163.com
Received Date: 2016-10-26
Rev Recd Date: 2017-02-20
Publish Date: 2017-12-27

Abstract

Land use structure optimization (LUSO) is an important issue for land use planning. In order for land use planning to have reasonable flexibility, uncertain optimization should be applied for LUSO. In this paper, the researcher first expounded the uncertainties of LUSO. Based on this, an interval programming model was developed, of which interval variables were to hold land use uncertainties. To solve the model, a heuristics based on Genetic Algorithm was designed according to Pareto Optimum principle with a confidence interval under given significance level to represent LUSO result. Proposed method was applied to a real case of Yangzhou, an eastern city in China. The following conclusions were reached. 1) Different forms of uncertainties ranged from certainty to indeterminacy lay in the five steps of LUSO, indicating necessary need of comprehensive approach to quantify them. 2) With regards to trade-offs of conflicted objectives and preferences to uncertainties, our proposed model displayed good ability of making planning decision process transparent, therefore providing an effective tool for flexible land use planning compiling. 3) Under uncertain conditions, land use planning effectiveness can be primarily enhanced by flexible management with reserved space to percept and hold uncertainties in advance.
- land use structure optimization (LUSO),
- uncertainties,
- flexibility,
- land use planning,
- decision support system,
- effectiveness

References

[1]	Berke P, Kaiser E J, 2006. Urban Land Use Planning (5th Edition). Urbana, US:University of Illinois Press, 45-168.
[2]	Brown J D, 2004. Knowledge, uncertainty and physical geography:towards the development of methodologies for questioning belief. Transactions of the Institute of British Geographers, 29(3):367-381. doi: 10.1111/j.0020-2754.2004.00342.x
[3]	Cao K, Huang B, Wang S W et al., 2012. Sustainable land use optimization using Boundary-based Fast Genetic Algorithm.Computers Environment and Urban Systems, 36(3):257-269.doi: 10.1016/j.compenvurbsys.2011.08.001
[4]	Cheng S, Chan C W, Huang G H, 2003. An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management. Engineering Applications of Artificial Intelligence, 16(5-6):543-554. doi: 10.1016/s0952-1976(03)00069-1
[5]	Chuai X W, Huang X J, Lai L et al., 2013. Land use structure optimization based on carbon storage in several regional terrestrial ecosystems across China. Environmental Science & Policy, 25(25):50-61. doi: 10.1016/j.envsci.2012.05.005
[6]	Costanza R, R D Groot, P Sutton et al., 2014. Changes in the global value of ecosystem services. Global Environmental Change, 26(1):152-158. doi:10.1016/j.gloenvcha.2014.04. 002
[7]	Dong C, Huang G H, Tan Q et al., 2014. Coupled planning of water resources and agricultural landuse based on an inexact-stochastic programming model. Frontiers of Earth Science, 8(1):70-80. doi: 10.1007/s11707-013-0388-5
[8]	Gao Q Z, Kang M Y, Xu H M et al., 2010. Optimization of land use structure and spatial pattern for the semi-arid loess hilly-gully region in China. Catena, 81(3):196-202. doi: 10.1016/j.catena.2010.03.002
[9]	Jiangsu Statistics Bureau, 2007-2014. Jiangsu Statistical Yearbook 2007-2014. Beijing:China Statistics Press. (in Chinese)
[10]	Li M, Guo P, Fang S Q et al., 2013. An inexact fuzzy parameter two-stage stochastic programming model for irrigation water allocation under uncertainty. Stochastic Environmental Research and Risk Assessment, 27(6):1441-1452. doi:10.1007/s 00477-012-0681-y
[11]	Li Xin, Ou Minghao, Liu Jiansheng et al., 2014. Regional land use structure optimization under uncertain theory. Transactions of the Chinese Society of Agricultural Engineering, 30(4):176-184. (in Chinese)
[12]	Li Xin, Ou Minghao, Yan Siqi et al., 2013. Calculation of elastic interval of land use structure based on interval optimization model. Transactions of the Chinese Society of Agricultural Engineering, 29(17):240-247. (in Chinese)
[13]	Li Y P, Huang G H, Chen X, 2011. Planning regional energy system in association with greenhouse gas mitigation under uncertainty. Applied Energy, 88(3):599-611. doi: 10.1016/j.apenergy.2010.07.037
[14]	Linkov I, Satterstrom F K, Kiker G et al., 2006. From comparative risk assessment to multi-criteria decision analysis and adaptive management:recent developments and applications.Environment International, 32(8):1072-1093. doi: 10.1016/j.envint.2006.06.013
[15]	Liu B D, 2015. Uncertainty Theory (4th Edition). Berlin, Heidelberg:Springer Berlin Heidelberg Press, 15-20.
[16]	Liu S F, Lin Y, 2006. Grey Prediction. London, UK:Springer London Press, 275-314.
[17]	Liu Y F, Ming D P, Yan J Y, 2002. Optimization of land use structure based on ecological GREEN equivalent. Geo-spatial Information Science, 5(4):60-67. doi: 10.1007/BF02826478
[18]	Liu Y, Qin X, Guo H et al., 2007. ICCLP:an inexact chanceconstrained linear programming model for land-use management of lake areas in urban fringes. Environmental management, 40(6):966-980. doi: 10.1007/s00267-007-9013-2
[19]	Liu Y, Yu Y J, Guo H C et al., 2009. Optimal land-use management for surface source water protection under uncertainty:a case study of Songhuaba watershed (Southwestern China).Water Resources Management, 23(10):2069-2083. doi: 10.1007/s11269-008-9370-5
[20]	Lu S S, Guan X L, Zhou M et al., 2014. Land resources allocation strategies in an urban area involving uncertainty:a case study of Suzhou, in the Yangtze River Delta of China. Environmental management, 53(5):894-912. doi: 10.1007/s00267-014-0247-5
[21]	Lu S S, Zhou M, Guan X L et al., 2015. An integrated GIS-based interval-probabilistic programming model for land-use planning management under uncertainty:a case study at Suzhou, China. Environmental Science and Pollution Research International, 22(6):4281-4296. doi: 10.1007/s11356-014-3659-0
[22]	Luo B, Li J B, Huang G H et al., 2006. A simulation-based interval two-stage stochastic model for agricultural nonpoint source pollution control through land retirement. Science of the Total Environment, 361(1-3):38-56. doi:10.1016/j.scitotenv.2005. 09.053
[23]	Matott L S, Babendreier J E, Purucker S T, 2009. Evaluating uncertainty in integrated environmental models:a review of concepts and tools. Water Resources Research, 45(6):735-742. doi: 10.1029/2008wr007301
[24]	Messina V, Bosetti V, 2003. Uncertainty and option value in land allocation problems. Annals of Operations Research, 124(1):165-181. doi: 10.1023/B:ANOR.0000004767.84402.b3
[25]	Nie X H, Huang G H, Wang D et al., 2008. Robust optimisation for inexact water quality management under uncertainty. Civil Engineering and Environmental Systems, 25(25):167-184.doi: 10.1080/10286600801908964
[26]	Polasky S, Carpenter S R, Folke C et al., 2011. Decision-making under great uncertainty:environmental management in an era of global change. Trends in Ecology & Evolution, 26(8):398-404. doi: 10.1016/j.tree.2011.04.007
[27]	Qiu B K, Lu S S, Zhou M et al., 2015. A hybrid inexact optimization method for land-use allocation in association with environmental/ecological requirements at a watershed level. Sustainability, 7(4):4643-4667. doi: 10.3390/su7044643
[28]	Refsgaard J C, van der Sluijs J P, Højberg A L et al., 2007. Uncertainty in the environmental modelling process:a framework and guidance. Environmental Modelling and Software, 22(11):1543-1556. doi: 10.1016/j.envsoft.2007.02.004
[29]	Sadeghi S H R, Jalili K, Nikkami D, 2009. Land use optimization in watershed scale. Land Use Policy, 26(2):186-193. doi: 10.1016/j.landusepol.2008.02.007
[30]	Singh K, 1999. Sustainable development:some reflections. Indian Journal of Agricultural Economics, 54:6-41.
[31]	Verburg P H, Tabeau A, Hatna E, 2013. Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis:a study for land use in Europe. Journal of Environmental Management, 127(S3):132-144. doi: 10.1016/j.jenvman.2012.08.038
[32]	Walker W E, Harremoës P, Rotmans J et al., 2003. Defining uncertainty:a conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4(1):5-17. doi: 10.1076/iaij.4.1.5.16466
[33]	Wang Hongrui, Gao Yuanyuan, Liu Qiong et al., 2010. Land use allocation based on interval multi-objective linear programming model:A case study of Pi County in Sichuan Province.Chinese Geographical Science, 20(2):176-183. doi:10.1007/s 11769-010-0176-z
[34]	Wang W Y, Zeng W H, 2013. Optimizing the regional industrial structure based on the environmental carrying capacity:an inexact fuzzy multi-objective programming model. Sustainability, 5(12):5391-5415. doi: 10.3390/su5125391
[35]	Wang X H, Yu S, Huang G H, 2004. Land allocation based on integrated GIS-optimization modeling at a watershed level.Landscape and Urban Planning, 66(2):61-74. doi: 10.1016/S0169-2046(03)00095-1
[36]	Yangzhou Statistics Bureau, 2007-2014. Yangzhou Statistical Yearbook 2007-2014. Beijing:China Statistics Press. (in Chinese)
[37]	Zhong T Y, Mitchell B, Huang X J, 2014. Success or failure:Evaluating the implementation of China's National General Land Use Plan (1997-2010). Habitat International, 44:93-101. doi: 10.1016/j.habitatint.2014.05.003
[38]	Zhou M, 2015. An interval fuzzy chance-constrained programming model for sustainable urban land-use planning and land use policy analysis. Land Use Policy, 42(42):479-491. doi: 10.1016/j.landusepol.2014.09.002
[39]	Zhou Min, Cai Yunlong, Guan Xingliang et al., 2014. A hybrid inexact optimization model for land-use allocation of China.Chinese Geographical Science, 25(1):62-73. doi:10.1007/s 11769-014-0708-z

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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An Uncertain Programming Model for Land Use Structure Optimization to Promote Effectiveness of Land Use Planning

1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China;

2. Department of Earth and Environmental Sciences, University of British Columbia, Okanagan Campus, Kelowna V1V1V7, Canada

Funds: Under the auspices of National Natural Science Foundation of China (No. 41401627, 41471144), Foundation Research Project of Jiangsu Province (No. BK20140236)

Corresponding author: MA Xiaodong.E-mail:xiaodgma@163.com

Received Date: 2016-10-26

Rev Recd Date: 2017-02-20

Publish Date: 2017-12-27

Key words:

land use structure optimization (LUSO) /
uncertainties /
flexibility /
land use planning /
decision support system /
effectiveness

Abstract: Land use structure optimization (LUSO) is an important issue for land use planning. In order for land use planning to have reasonable flexibility, uncertain optimization should be applied for LUSO. In this paper, the researcher first expounded the uncertainties of LUSO. Based on this, an interval programming model was developed, of which interval variables were to hold land use uncertainties. To solve the model, a heuristics based on Genetic Algorithm was designed according to Pareto Optimum principle with a confidence interval under given significance level to represent LUSO result. Proposed method was applied to a real case of Yangzhou, an eastern city in China. The following conclusions were reached. 1) Different forms of uncertainties ranged from certainty to indeterminacy lay in the five steps of LUSO, indicating necessary need of comprehensive approach to quantify them. 2) With regards to trade-offs of conflicted objectives and preferences to uncertainties, our proposed model displayed good ability of making planning decision process transparent, therefore providing an effective tool for flexible land use planning compiling. 3) Under uncertain conditions, land use planning effectiveness can be primarily enhanced by flexible management with reserved space to percept and hold uncertainties in advance.

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Reference (39)

[1]	Berke P, Kaiser E J, 2006. Urban Land Use Planning (5th Edition). Urbana, US:University of Illinois Press, 45-168.
[2]	Brown J D, 2004. Knowledge, uncertainty and physical geography:towards the development of methodologies for questioning belief. Transactions of the Institute of British Geographers, 29(3):367-381. doi:10.1111/j.0020-2754.2004.00342.x
[3]	Cao K, Huang B, Wang S W et al., 2012. Sustainable land use optimization using Boundary-based Fast Genetic Algorithm.Computers Environment and Urban Systems, 36(3):257-269.doi:10.1016/j.compenvurbsys.2011.08.001
[4]	Cheng S, Chan C W, Huang G H, 2003. An integrated multi-criteria decision analysis and inexact mixed integer linear programming approach for solid waste management. Engineering Applications of Artificial Intelligence, 16(5-6):543-554. doi:10.1016/s0952-1976(03)00069-1
[5]	Chuai X W, Huang X J, Lai L et al., 2013. Land use structure optimization based on carbon storage in several regional terrestrial ecosystems across China. Environmental Science & Policy, 25(25):50-61. doi:10.1016/j.envsci.2012.05.005
[6]	Costanza R, R D Groot, P Sutton et al., 2014. Changes in the global value of ecosystem services. Global Environmental Change, 26(1):152-158. doi:10.1016/j.gloenvcha.2014.04. 002
[7]	Dong C, Huang G H, Tan Q et al., 2014. Coupled planning of water resources and agricultural landuse based on an inexact-stochastic programming model. Frontiers of Earth Science, 8(1):70-80. doi:10.1007/s11707-013-0388-5
[8]	Gao Q Z, Kang M Y, Xu H M et al., 2010. Optimization of land use structure and spatial pattern for the semi-arid loess hilly-gully region in China. Catena, 81(3):196-202. doi:10.1016/j.catena.2010.03.002
[9]	Jiangsu Statistics Bureau, 2007-2014. Jiangsu Statistical Yearbook 2007-2014. Beijing:China Statistics Press. (in Chinese)
[10]	Li M, Guo P, Fang S Q et al., 2013. An inexact fuzzy parameter two-stage stochastic programming model for irrigation water allocation under uncertainty. Stochastic Environmental Research and Risk Assessment, 27(6):1441-1452. doi:10.1007/s 00477-012-0681-y
[11]	Li Xin, Ou Minghao, Liu Jiansheng et al., 2014. Regional land use structure optimization under uncertain theory. Transactions of the Chinese Society of Agricultural Engineering, 30(4):176-184. (in Chinese)
[12]	Li Xin, Ou Minghao, Yan Siqi et al., 2013. Calculation of elastic interval of land use structure based on interval optimization model. Transactions of the Chinese Society of Agricultural Engineering, 29(17):240-247. (in Chinese)
[13]	Li Y P, Huang G H, Chen X, 2011. Planning regional energy system in association with greenhouse gas mitigation under uncertainty. Applied Energy, 88(3):599-611. doi:10.1016/j.apenergy.2010.07.037
[14]	Linkov I, Satterstrom F K, Kiker G et al., 2006. From comparative risk assessment to multi-criteria decision analysis and adaptive management:recent developments and applications.Environment International, 32(8):1072-1093. doi:10.1016/j.envint.2006.06.013
[15]	Liu B D, 2015. Uncertainty Theory (4th Edition). Berlin, Heidelberg:Springer Berlin Heidelberg Press, 15-20.
[16]	Liu S F, Lin Y, 2006. Grey Prediction. London, UK:Springer London Press, 275-314.
[17]	Liu Y F, Ming D P, Yan J Y, 2002. Optimization of land use structure based on ecological GREEN equivalent. Geo-spatial Information Science, 5(4):60-67. doi:10.1007/BF02826478
[18]	Liu Y, Qin X, Guo H et al., 2007. ICCLP:an inexact chanceconstrained linear programming model for land-use management of lake areas in urban fringes. Environmental management, 40(6):966-980. doi:10.1007/s00267-007-9013-2
[19]	Liu Y, Yu Y J, Guo H C et al., 2009. Optimal land-use management for surface source water protection under uncertainty:a case study of Songhuaba watershed (Southwestern China).Water Resources Management, 23(10):2069-2083. doi:10.1007/s11269-008-9370-5
[20]	Lu S S, Guan X L, Zhou M et al., 2014. Land resources allocation strategies in an urban area involving uncertainty:a case study of Suzhou, in the Yangtze River Delta of China. Environmental management, 53(5):894-912. doi:10.1007/s00267-014-0247-5
[21]	Lu S S, Zhou M, Guan X L et al., 2015. An integrated GIS-based interval-probabilistic programming model for land-use planning management under uncertainty:a case study at Suzhou, China. Environmental Science and Pollution Research International, 22(6):4281-4296. doi:10.1007/s11356-014-3659-0
[22]	Luo B, Li J B, Huang G H et al., 2006. A simulation-based interval two-stage stochastic model for agricultural nonpoint source pollution control through land retirement. Science of the Total Environment, 361(1-3):38-56. doi:10.1016/j.scitotenv.2005. 09.053
[23]	Matott L S, Babendreier J E, Purucker S T, 2009. Evaluating uncertainty in integrated environmental models:a review of concepts and tools. Water Resources Research, 45(6):735-742. doi:10.1029/2008wr007301
[24]	Messina V, Bosetti V, 2003. Uncertainty and option value in land allocation problems. Annals of Operations Research, 124(1):165-181. doi:10.1023/B:ANOR.0000004767.84402.b3
[25]	Nie X H, Huang G H, Wang D et al., 2008. Robust optimisation for inexact water quality management under uncertainty. Civil Engineering and Environmental Systems, 25(25):167-184.doi:10.1080/10286600801908964
[26]	Polasky S, Carpenter S R, Folke C et al., 2011. Decision-making under great uncertainty:environmental management in an era of global change. Trends in Ecology & Evolution, 26(8):398-404. doi:10.1016/j.tree.2011.04.007
[27]	Qiu B K, Lu S S, Zhou M et al., 2015. A hybrid inexact optimization method for land-use allocation in association with environmental/ecological requirements at a watershed level. Sustainability, 7(4):4643-4667. doi:10.3390/su7044643
[28]	Refsgaard J C, van der Sluijs J P, Højberg A L et al., 2007. Uncertainty in the environmental modelling process:a framework and guidance. Environmental Modelling and Software, 22(11):1543-1556. doi:10.1016/j.envsoft.2007.02.004
[29]	Sadeghi S H R, Jalili K, Nikkami D, 2009. Land use optimization in watershed scale. Land Use Policy, 26(2):186-193. doi:10.1016/j.landusepol.2008.02.007
[30]	Singh K, 1999. Sustainable development:some reflections. Indian Journal of Agricultural Economics, 54:6-41.
[31]	Verburg P H, Tabeau A, Hatna E, 2013. Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis:a study for land use in Europe. Journal of Environmental Management, 127(S3):132-144. doi:10.1016/j.jenvman.2012.08.038
[32]	Walker W E, Harremoës P, Rotmans J et al., 2003. Defining uncertainty:a conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4(1):5-17. doi:10.1076/iaij.4.1.5.16466
[33]	Wang Hongrui, Gao Yuanyuan, Liu Qiong et al., 2010. Land use allocation based on interval multi-objective linear programming model:A case study of Pi County in Sichuan Province.Chinese Geographical Science, 20(2):176-183. doi:10.1007/s 11769-010-0176-z
[34]	Wang W Y, Zeng W H, 2013. Optimizing the regional industrial structure based on the environmental carrying capacity:an inexact fuzzy multi-objective programming model. Sustainability, 5(12):5391-5415. doi:10.3390/su5125391
[35]	Wang X H, Yu S, Huang G H, 2004. Land allocation based on integrated GIS-optimization modeling at a watershed level.Landscape and Urban Planning, 66(2):61-74. doi:10.1016/S0169-2046(03)00095-1
[36]	Yangzhou Statistics Bureau, 2007-2014. Yangzhou Statistical Yearbook 2007-2014. Beijing:China Statistics Press. (in Chinese)
[37]	Zhong T Y, Mitchell B, Huang X J, 2014. Success or failure:Evaluating the implementation of China's National General Land Use Plan (1997-2010). Habitat International, 44:93-101. doi:10.1016/j.habitatint.2014.05.003
[38]	Zhou M, 2015. An interval fuzzy chance-constrained programming model for sustainable urban land-use planning and land use policy analysis. Land Use Policy, 42(42):479-491. doi:10.1016/j.landusepol.2014.09.002
[39]	Zhou Min, Cai Yunlong, Guan Xingliang et al., 2014. A hybrid inexact optimization model for land-use allocation of China.Chinese Geographical Science, 25(1):62-73. doi:10.1007/s 11769-014-0708-z

An Uncertain Programming Model for Land Use Structure Optimization to Promote Effectiveness of Land Use Planning

doi: 10.1007/s11769-017-0896-4

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References

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通讯作者: 陈斌, bchen63@163.com

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