Usability of the Various Land Use Optimization Models for the Spatial Planning Purposes in the Republic of North Macedonia
DOI:
https://doi.org/10.61326/silvaworld.v3i2.279Keywords:
Generic algorithm, Heuristic, Land use optimizationAbstract
The land use optimization process uses different models that generally belong to 2 groups: mathematical models and heuristics models. Variety of models shave been developed for various aims. Each of has its positive and negative sides, advantages and disadvantages. Aim of this research is to be defined usability of various land use model optimization for the purpose of spatial planning in the Republic of North Macedonia. The basic methodological tool in this chapter covers collection, study and comparative analysis of relevant literary data and evaluation of selected researches. In the absence of domestic literature, mostly literature from several countries from Europe, USA, Asia was analyzed. Then 17 models were selected for in detail analyze. To examine the possibilities of applying the optimization models described in the analyzed research. an evaluation was performed according to the following criteria: a) availability of the data used by the model, that is, the possibility of providing the data used in the model from official sources; b) number of optimization goals; c) number of land uses; d) local adaptability, which implies conformity of the goals, purposes, influencing factors, limitations and other specifics of the model with those in our conditions; e) scope of the research. More of the The reviewed papers on the analyzed optimization models and methods, marked by the authors, clearly indicate the fact or exceptional difficulty in determining the optimal land use model.
References
Blinkov, I. (2011). Modeling of processes, internal scripts for students, on Macedonian language. UKIM-HEF.
Blinkov, I., Mincev, I., & Trendafilov, B. (2011). Use of modern geomatic techniques for erosion modelling. UKIM-HEF.
Cao, K., Batty, M., Huang, B., Liu, Y., Yu, L., & Chen, J. (2011). Spatial multi-objective land use optimization: Extensions to the non-dominated sorting genetic algorithm-II. International Journal of Geographical Information Science, 25(12), 1949-1969. https://doi.org/10.1080/13658816.2011.570269
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
Chen, Y., Yang, K., Zhou, D., Qin, J., & Guo, X. (2010). Improving the Noah land surface model in arid regions with an appropriate parameterization of the thermal roughness length. Journal of Hydrometeorology, 11(4), 995-1006. https://doi.org/10.1175/2010jhm1185.1
Crohn, D. M., & Thomas, A. C. (1998). Mixed-integer programming approach for designing land application systems at a regional scale. Journal of Environmental Engineering, 124(2), 170-177. https://doi.org/10.1061/(ASCE)0733-9372(1998)124:2(170)
De Groot, R. (2006). Function-analysis and valuation as a tool to assess land use conflicts in planning for sustainable, multi-functional landscapes. Landscape and Urban Planning, 75(3-4), 175-186. https://doi.org/10.1016/j.landurbplan.2005.02.016
Ferrand, N. (1996). Modelling and supporting multi-actor spatial planning using multi-agents systems. Third International Conference Integrating Gis and Environmental Modeling. Santa Fe.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2/3), 9599. https://doi.org/10.1023/a:1022602019183
Greenberg, M. (1999). Land use allocation optimization models applied to future use at the U.S. doe’s major nuclear weapons sites. Report 31, Consortium for Risk Evaluation with Stakeholder Participation (CRESP).
Heady, E. O., & Hall, H. H. (1968). Linear and nonlinear spatial models in agricultural competition, land use, and production potential. American Journal of Agricultural Economics, 50(5), 1539. https://doi.org/10.2307/1237353
Legriel, J., Le Guernic, C., Cotton, S., & Maler, O. (2010). Approximating the pareto front of multi-criteria optimization problems. In J. Esparza & R. Majumdar (Eds), Tools and algorithms for the construction and analysis of systems (pp. 69-83). Springer. https://doi.org/10.1007/978-3-642-12002-2_6
Ligmann‐Zielinska, A., Church, R. L., & 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
Loonen, W., & Koomen, E. (2007). Calibration and validation of the Land Use Scanner allocation algorithms. Milieu- en Natuurplanbureau.
Mansor, S. B., Pormanafi, S., Mahmud, A. R. B., & Pirasteh, S. (2012). Optimization of land use suitability for agriculture using integrated geospatial model, and genetic algorithms. XXII ISPRS Congress. Melbourne.
Mendoza, G. A. (1999). A GIS-based multicriteria approaches to land use suitability assessment and allocation. Seventh Symposium on Systems Analysis in Forest Resources. Traverse City.
Mincev, I. (2007). GIS aided multi objective allocation of areas with intensive wood production and maintaining fauna biodiversity. International Symposium Sustainable Forestry - Problems and Challenges Perspectives and Challenges in Wood Technology. Ohrid.
Miranda, J. I. (2004). A solution to the land allocation problem integrating multicriteria analysis, fuzzy logic, and GIS. Revista Brasileira de Agroinformatica, 6(2), 103-117.
Rutten, M., van Rooij, W., & Van Dijk, M. (2012). Global-to-local modelling of land use dynamics in Vietnam, Potential effects of high climate impact and high economic growth scenarios. 15th Annual Conference on Global Economic Analysis "New Challenges for Global Trade and Sustainable Development". Geneva.
Santorineou, A., Hatzopoulos, J., Siakavara, K., & Davos, C. (2008). Spatial conflict management in urban planning. https://www.researchgate.net/publication/254463762_SPATIAL_CONFLICT_MANAGEMENT_IN_URBAN_PLANNING
Shifa, M., Jianhua, H., & Feng, L. (2009). Land-use spatial optimization model based on particle swarm optimization. ISPRS Wuhan 2009 Workshop Virtual Changing Globe for Visualisation and Analysis. Wuhan.
Stewart, T. J., Janssen, R., & van Herwijnen, M. (2004). A genetic algorithm approach to multiobjective land use planning. Computers & Operations Research, 31(14), 2293-2313. https://doi.org/10.1016/S0305-0548(03)00188-6
Van Delden, H., McDonald, G., Shi, Y., Hurkens, J., Van Vliet, J., Marjan Van Den Belt, M. (2011). Integrating socio-economic and land-use models to support urban and regional planning. Conference: AGILE 2011. Salt Lake City.
Working Paper, Pine Nut Allotments (NV). (2009). Land use and development procedural plan, land use suitability analysis (printed copy without other data).
Yaolin, L. Man, Y., JianHua, H., & Jing, Q. (2013). Model of land use spatial optimization based on a knowledge guide genetic algorithm. Proceedings of the 12th International Conference on GeoComputation, LIESMARS. Wuhan.
Zhang, H. H., Zeng, Y. N., & Bian, L. (2010). Simulating multi-objective spatial optimization allocation of land use based on the integration of multi-agent system and genetic algorithm. International Journal of Environmental Research, 4(4), 765-776.
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