Land use optimization using the fuzzy mathematical-spatial approach: a case study of Chelgerd watershed, Iran
In recent years, inappropriate land use, urban and industrial development along with different pollutions emanating from it gives rise to loss of natural resources and further leads to destructive floods, soil erosion, sedimentation and other various environmental, economic and social damages. Thus, management and planning are essential for the proper utilization, protection and revival of these resources. This study aimed to develop a mathematical-spatial optimum utilization model using FGP – MOLA in watershed including environmental and economic objectives while considering social issues. The results showed that the proposed model can lead to economic growth to 37% and decreasing the environmental damages to 2.4%. Under optimized condition, the area allocated to dry farming lands will decrease about 12% and gardens will increase about 423% and the other land uses remain unchanged too. In addition to, the results demonstrated the usefulness and efficiency of the proposed fuzzy model due to its flexibility and capability to simultaneously provide both optimum values and location of production resources.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Akbari, N.; Zahedi Keyvan, M. 2007. Application of fuzzy logic in determination of suitable crop cultivation pattern in a farm (Approach: fuzzy goal programming), Journal of Agricultural Economics 1(2): 13–35 (in Persian).
Allen, R. G.; Pereira, L. S.; Raes, D.; Smith, M. 1998. Crop evapotranspiration: guide-lines for computing crop water requirements, in FAO Irrigation and Drainage Paper No. 56. FAO, Rome, Italy.
Alphan, H.; Guvensoy, L. 2016. Detecting coastal urbanization and land use change in southern Turkey, Journal of Environmental Engineering and Landscape Management 24: 97–107. https://doi.org/10.3846/16486897.2015.1113976
Amini, A. 2013. Planning and optimal allocation of agricultural production resources under uncertainty; application of multiobjective fuzzy goal programming approach, Geography and Environmental Planning Journal 51(3): 107–128 (in Persian).
Asadpoor, H.; Khalilian, S.; Peykani, Gh. 2005. Theory and application of linear fuzzy goal programming model in crop cultivation pattern optimization, Journal of Agricultural Economics and Development 309: 307–338 (in Persian).
Benjamin, M. 2001. Land use conflicts resolution in a fragile ecosystem using Multi-Criteria Evaluation (MCE) and a GISBased Decision Support System (DSS), in International Conference on Spatial Information for Sustainable Development, 2–5 October 2001, Nairobi, Kenya. 11 p.
Biswas, A.; Pal, B. B. 2005. Application of fuzzy goal programming technique to land use planning in agricultural system, Omega Journal 33: 391–398. https://doi.org/10.1016/j.omega.2004.07.003
Eastman, J. R.; James, T.; Weigen, A.; Peter, A.; Kyem, K. 1995. Raster procedures for multi-criteria/multi-objective decisions, Photogrammetric Engineering & Remote Sensing 61(5): 539–547.
FAO. 1976. A Framework for Land Evaluation. Soils Bulletin 32, Rome.
FAO. 1983. Guidelines: Land Evaluation for Rainfed Agriculture. Soils Bulletin 52, Rome.
FAO. 1984. Land Evaluation for Forestry. Forestry Paper 48, Rome.
FAO. 1985. Guidelines: Land Evaluation for Irrigated Agriculture. Soils Bulletin 55, Rome.
FAO. 1990. Guidelines: Land Evaluation for Extensive Grazing. Soils Bulletin 58, Rome.
FAO. 1992. Guidelines for land use planning. Prepared by the Interdepartmental Working Group on Land Use Planning. Soils Bulletin 66, Rome.
Fooks, J. R.; Messer, K.D. 2012. Maximizing conservation and in-kind cost share: Applying Goal Programming to forest protection, Journal of Forest Economics 18: 207–217. https://doi.org/10.1016/j.jfe.2012.04.001
Graymore, M. L. M.; Wallis, A. M.; Richards, A. J. 2009. An index of regional sustainability: a GIS-Based multiple criteria analysis decision support system for progressing sustainability, Ecological Complexity Journal 6: 453–462. https://doi.org/10.1016/j.ecocom.2009.08.006
Han, Y.; Huang, Y. F.; Wang, G. Q.; Maqsood, I. 2011. A multiobjective linear programming model with interval parameters for water resources allocation in Dalian city, Water Resource Management 25: 449–463. https://doi.org/10.1007/s11269-010-9708-7
Haque, A.; Asami, Y. 2014. Optimizing urban land use allocation for planners and real estate Developers, Computers, Environment and Urban Systems 46: 57–69. https://doi.org/10.1016/j.compenvurbsys.2014.04.004
Haregeweyn, N.; Berhe, A.; Tsunekawa, A.; Tsubo, M.; Tsegaye Meshesha, D. 2012. Integrated watershed management as an effective approach to curb land degradation: a case study of the enabered watershed in Northern Ethiopia, Environmental Management 50: 1219–1233. https://doi.org/10.1007/s00267-012-9952-0
Harshada, R.; Bhede, M.; Arati, S. P. 2015. A study of land use planning and optimization, International Journal of Modern Trends in Engineering and Research 2(7): 956–964.
Jereon, M.; Anton, V. R.; Tim, Q.; Manuel, M.; Christian, P.; Dominique, A. 2013. Predicting future spatial distribution of SOC across entire France, Geophysical Research Abstracts, 15, 1 P.
Lehmann, N.; Briner, S.; Finger, R. 2013. The impact of climate and price risks on agricultural land use and cropmanagement decisions, Land Use Policy 35: 119–130. https://doi.org/10.1016/j.landusepol.2013.05.008
Li, C.; Zhao, J. 2017. Assessment of future urban growth impact on landscape pattern using cellular automata model: a case study of Xuzhou city, China, Journal of Environmental Engineering and Landscape Management 25: 23–38. https://doi.org/10.3846/16486897.2016.1187620
Liang, H.; Chen, D.;Zhang, Q. 2017. Assessing Urban Green Space distribution in a compact megacity by landscape metrics, Journal of Environmental Engineering and Landscape Management 25: 64–74. https://doi.org/10.3846/16486897.2016.1210157
Liu, Y.; Jiao, L.; Liu, Y.; He, J. 2013. A self-adapting fuzzy inference system for the evaluation of agricultural land, Environmental Modelling and Software Journal 40: 226–234. https://doi.org/10.1016/j.envsoft.2012.09.013
Maheshwari, B. 2016. Understanding the performance of irrigation systems around homes, Journal of Environmental Engineering and Landscape Management 24: 278–292. https://doi.org/10.3846/16486897.2016.1176575
Memmah, M. M.; Lescourret, F.; Yao, X.; Lavigne, C. 2015. Metaheuristics for agricultural land use optimization. A review. 24 p.
Mohammadi, M.; Nastaran, M.; Sahebgharani, A. 2015. Sustainable spatial land use optimization through Non-Dominated Sorting Genetic Algorithm-II (NSGA-II): (Case Study: Baboldasht District of Isfahan), Indian Journal of Science and Technology 8(3): 118–129. https://doi.org/10.17485/ijst/2015/v8iS3/60700
Shirazi, S. M.; Adham, MD I.; Othman, F.; Zardari, N. H.; Ismail, Z. 2016. Runoff trend and potentiality in Melaka Tengah catchment of Malaysia using SCS-CN and statistical technique, Journal of Environmental Engineering and Landscape Management 24: 245–257. https://doi.org/10.3846/16486897.2016.1184153
Pajoohesh, M.; Gorji, M.; Taheri, m.; Sarmadiyan, F.; Mohamadi, j.; Samadi, H. 2011. Effect of land use on sediment yield using GIS in Zayandehrood upstream basin, Iran Water Research Journal 5(8): 143–152 (in Persian).
Pal, B. B.; Moitra, B. N.; Maulik, U. 2003. A goal programming procedure for fuzzy multi-objective linear fractional programming problem, Fuzzy Sets and Systems 139: 395–405. https://doi.org/10.1016/S0165-0114(02)00374-3
Porta, J.; Parapar, J.; Doallo, R.; Rivera, F. F.; Sante, I.; Crecente, R. 2013. High performance genetic algorithm for land use planning, Computers, Environment and Urban Systems 37: 45–58. https://doi.org/10.1016/j.compenvurbsys.2012.05.003
Renfro, G. W. 1975. Use of erosion equations and sediment delivery ratios for predicting sediment yield. In Present and Prospective technology for predicting sediment yields and sources, Agricultural Resources Services, ARS-S-40, US Dept. Agric., Washington, D.C. 33–45.
Rockstrom, J.; Karlberg, L. 2010. Managing Water in Rain-fed Agriculture- The need for a paradigm shift, Agricultural Water Management97(4): 543–550. https://doi.org/10.1016/j.agwat.2009.09.009
Sahnoun, H.; Serbaji, M. M.; Karray B.; Medhioub, K. 2012. GIS and multi-criteria analysis to select potential sites of agroindustrial complex, Environmental Earth Sciences 66(8): 2477–2489. https://doi.org/10.1007/s12665-011-1471-4
Segura, M.; Ray, B. D.; Maroto, C. 2014. Decision support systems for forest management: a comparative analysis and assessment, Computers and Electronics in Agriculture Journal 101: 55–67. https://doi.org/10.1016/j.compag.2013.12.005
Shaygan, M.; Alimohammadi, A.; Mansourian, A.; Shams Govara, Z.; Kalami, S. M. 2014. Spatial multi-objective optimization approach for land use allocation using NSGA-II, Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(3): 906–916. https://doi.org/10.1109/ JSTARS.2013.2280697
Stewart, T. J.; Janssen, R. 2014. A multi-objective GIS-based land use planning algorithm, Computers, Environment and Urban Systems 46: 25–34. https://doi.org/10.1016/j.compenvurbsys.2014.04.002
Tiwari, R. N.; Dhamer, S.; Rao, J. R. 1986. Priority structure in fuzzy goal programming, Fuzzy Sets and Systems 19: 251–259. https://doi.org/10.1016/0165-0114(86)90054-0
Xiaoya, M.; Xiang, Z. 2015. Land use allocation based on a multiobjective artificial immune optimization model: an application in Anlu County, China, Sustainability 7:15 632 –15651. https://doi.org/10.3390/su71115632
Zhu, C.; Ji, P.; Li, S. 2017.Effects of urban green belts on the air temperature, humidity and air quality, Journal of Environmental Engineering and Landscape Management 25: 39–55. https://doi.org/10.3846/16486897.2016.1194276