A Genetic Algorithm Enhancement of MOLA Approach Using Landscape Metrics

Document Type : Research Paper

Authors

1 PhD Student, Gorgan University of Agricultural Science & Natural Resource, Gorgan, Iran

2 Associate Professor, Gorgan University of Agricultural Science & Natural Resource, Gorgan, Iran

3 Assistant Professor, Engineering & Technological Collage, University of Golestan, Iran

Abstract

There is competition between land uses in spatial land use allocation. In this research two approaches including Multi Objective Land Allocation (MOLA) and optimization with Genetic Algorithm (GA) were used for conflict resolution. The MOLA approach is based on suitability and proximity to ideal point whereas in GA, a suitability layer together with a cohesion index was used for land use allocation. The output from MOLA application was fed into the GA approach as the initial population and contiguity index as a landscape metric was used in the process to improve the result. With inclusion of contiguity in the GA approach which is absent in MOLA, and has no precedence in Iran, the final patches in the land use pattern were compacter and better shaped. Results showed GA application using MOLA output improves landscape metrics specifications in the final land use plan. However, including landscape metrics compromises suitability for land use, but there is possibility of balancing suitability and landscape indices in the GA application process. The final layer created through GA showed capability of considering suitability and landscape metrics simultaneously in land use planning towards achieving an optimal solution.

Keywords

References

 
1. سلمان ماهینی، عبدالرسول (1392). «طرح آمایش استان گلستان». گزارش فاز اول، استانداری گلستان.
2. سلمان ماهینی، عبدالرسول؛ کامیاب، حمیدرضا (1390). سنجش از دور و سامانه­های اطلاعات جغرافیایی با نرم­افزار ایدریسی. چاپ دوم، تهران، انتشارات مهر مهدیس.
3. شایگان، مهران؛ علیمحمدی، عباس؛ منصوریان، علی (1391). «بهینه­سازی چندهدفة تخصیص کاربری اراضی با استفاده از الگوریتم NSGA-II»، فصل‌نامة سنجش از دور و GIS ایران، سال چهارم، شمارة دوم، صفحات 18-1.
4. Aerts, Jeroen; Heuvelin, Gerard (2002). "Using simulated annealing for resource allocation". International Journal of Geographical Information Science,16(6), 571-587.
5. Aerts, Jeroen; Eisinger, Erwin; Heuvelink, Gerard. B. M; Stewart, Theodor (2003). "Using linear integer programming for multi-site land-use allocation". Geographical Analysis, 35(2), 148–169.
6. Aerts, Jeroen; Van Herwijnen, Marjan; Janssen, Ron; Stewart, Theodor. J. (2005). "Evaluating spatial design techniques for solving land-use allocation problems". Journal ofEnvironmental Planning and Management, 48(1), 121–142.
7. Bettinger, Pete; Graetz, David; Boston, Kevin; Sessions, John;Chung, Woodam (2002). "Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems". Silva Fennica, 36(2), 561–584.
8. Cao, Kia; Huang, Bo;, Wang Shawen; Lin, Hui. "Sustainable land use optimization using Boundary-based Fast Genetic Algorithm", Computers, Environment and Urban Systems, 36, 257-269.
9. Cao, Kia; Huang, Bo; Zhao, Qing; Wang, Shengxiao, (2009). "Land use allocation optimization towards sustainable development based on genetic algorithm". In Geoinformatics, 2009 17th international Conference. Fairfax, USA. 1–5.
10. Cao, Kia., Batty, Michael., Huang, Bo., Liu, Yan., Yu, Le, and Jiongfeng, Chen (2011). " Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II, "International Journal of Geographical Information Science, 1-21.
11. Carver, Stephen. J. (1991). "Integrating multi-criteria evaluation with geographical information systems". International Journal of Geographical Information Science, 5(3), 321-339.
12. Duh, Jiunn-Der; Brown, Daniel. G. (2007). "Knowledge-informed pareto simulated annealing for multi-objective spatial allocation". Computers, Environment and UrbanSystems, 31, 235–281.
13. Feng, Cheng-Min; Lin, Jen-Jia (1999). "Using a genetic algorithm to generate alternative sketch maps for urban planning". Computers, Environment and Urban Systems, 23, 91–108.
14. Goldberg, David E. (1989).Genetic algorithms in search, optimization, and machine learning. Boston, MA: Addison-Wesley Longman.
15. Holland, John-Henry (1975).Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press.
16. Holzka¨mper, Annelie; Lausch, Angela; Seppelt, Ralf (2006). "Optimizing landscape configuration to enhance habitat suitability for species with contrasting habitat requirements". Ecological Modelling, 198, 277-292.
17. Huston Michel(2006). The Need for Science and Technology in Land Management, Online Book: The International Development Research Centre. http://www.idrc.ca/en/ev-29587-201-1-DO_TOPIC.html (Last Visit: 08.2014).
18. Janssen, Ron; Van Herwijnen, Marjan; Stewart, Theodor. J.; Aerts, Jeroen. C. J. H. (2008). "Multi objective decision support for land-use planning. Environment and Planning " B: Planningand Design, 35, 740–756.
19. Liu, Xiaoping;, Ou, Jinpei; Ai, Bin (2013). "Combining system dynamics and hybrid particle swarm optimization for land use allocation", Ecological Modeling, 257, 11-24.
20. Matthews, K. B.; Buchan, K.;Sibbald, A. R.; Craw, Susan (2006). "Combining deliberative and computer-based methods for multi-objective land-use planning". Agricultural Systems, 87, 18–37.
21. McGarigal, Kewin; Cushman, Sam. A.; Neel, Maile. C.; Ene, Eduard (2002). "FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps". University of Massachusetts, Amherst. Available from: <www.umass.edu/landeco/research/fragstats/fragstats.html> (Last Visit: 09.2014)
22. Michalewicz, Zbigniew (1996). Genetic algorithms+data structures=evolution programs. Berlin, Springer.
23. Mitchell, Melanie; Crutchfield, James P.; Hraber, Peter T. (1994). "Evolving cellular automata to perform computations: Mechanisms and impediments". Physica D, 75, 361–391.
24. Porta, Juan; Paraper, Jorge; Doallo, Ramon; Rivera, Francisco; Sante, Ines; Crecente, Rafael (2013). "High performance genetic algorithm for land use planning". Computers, Environment and Urban Systems, 37, 45-58.
25. Stewart, Theodor. J.; Janssen, Ron.; Van Herwijnen, Marjan (2004). "A genetic algorithm approach to multi objective land use planning". Computers & Operations Research, 31, 2293–2313.
 
 
 

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History

  • Receive Date: 18 September 2014
  • Revise Date: 01 September 2015
  • Accept Date: 14 April 2015

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