Developing the optimal model for correct use of environmental resources in chelgerd watershed

Document Type : Original Article


1 Department of Watershed Management, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran

2 Department of Applied Mathematics, Faculty of Basic Sciences, Shahrekord University, Shahrekord, Iran


Over the past few decades, several models such as the LP model have been used for watershed management and planning as well as determining optimal cultivation pattern for agricultural planning. Management and planning methods are the most important applied tools of management science for optimal allocating of environmental resources, in order to gain the most profits in the different fields including natural resources, water resources and etc. The purpose of this study was to develop an optimal land use model with an environmental approach using a combination of linear programming mathematical optimization method to determine the optimal land use area by spatial optimization method of multi objective land allocation to determine The optimal location of land uses.
Materials and methods:
In this study using optimization methods tried to produce an environmental model which is compatible with social and economic condition of watershed for better use, conservation and rehabilitation of existent natural resources. For this purpose, a single objective linear programming model with environmental approach was used to land use optimization and a multi objective optimization model for optimal allocation of resources.
Results and discussion:
After collecting and analyzing the data and entering them into the single-objective optimization model of linear programming, a mathematical model was first developed that determines the optimal area of land uses in the basin, and then, using the spatial optimization model of multi-objective land allocation the optimal land uses location was determined. The proposed combined model is an efficient model because it can simultaneously determine the optimized area and location of land uses.
The results showed that the proposed combined model can be the base for correct management of resources and decrease the amount of soil erosion to 9 percent and increase the amount of profit to 96 percent.


  1. Abubakar, A.M., Efron, N.G. and Joseph O.A., 2012. Remote sensing and gis based predictive model for desertification early warning in north eastern Nigeria,. NED University Journal of Research. 4(1), 1-14.
  2. Alansi, A.W., Amin, M.S.M., Abdul Halim, G., Shafri, H.Z.M., Thamer, A.M., Waleed, A.R.M., AimrunW. and Ezrin, M.H., 2009. The effect of development and land use change on rainfall-runoff and runoff-sediment relationships under humid tropical condition: case study of Bernam watershed Malaysia. European Journal of Scientific Research. 31, 88-105.
  3. Arkhi, S., Yoosefi, S. and Rostamizad, Gh., 2013. Survey the effect of land use optimization in decreasing erosion and sedimentation of Chamgardlan dam watershed using GIS, Geography and Territorial Spatial Arrangement, 6: 75-84. [In Persian]
  4. Benjamin, M., 2001. Land use conflicts resolution in a fragile ecosystem using multi-criteria evaluation (MCE) and a GIS-Based decision support system (DSS), International Conference on Spatial Information for Sustainable Development, Nairobi, Kenya. p11.
  5. Chamheydar, H., Nhkkami, D., Pazira, A. and Ghafouri. M., 2011. Soil loss minimization through land use optimization. World Applied Science Journal. 12, 76-82.
  6. Demir, Y.M., Atasoy, M., Bayrak, T. and Biyik, C., 2008. Evaluating sustainable land use for the De Irmendere valley: a case study from northeastern Turkey, Journal of Sustainable Development and World Ecology.14, 626-633.
  7. Eastman, J.R., James, T., Weigen, A., Peter A. and Kyem. K., 1995.Raster procedures for multi-criteria/multi-objective decisions, Photogrammetric Engineering and Remote Sensing. 61(5), 539-547.
  8. Fooks, J.R. and Messer. K.D., 2012. Maximizing conservation and in-kind cost share: applying goal programming to forest protection, Journal of Forest Economics. 18, 207–217.
  9. Hamdi, A.T., 2002. Operations research: an introduction, Eighth Edition, University of Arkansas, Fayetteville, 840 p.
  10. Haque, A. and Asami. Y., 2014. Optimizing urban land use allocation for planners and real estate Developers, Computers, Environment and Urban Systems.46, 57–69.
  11. Harshada, R., Bhede, M. and 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.
  12. Honarbakhsh, A., Pajoohesh, M., Zangiabadi M. and Heydari, M., 2016. Land use optimization using combination of fuzzy linear programming and multi objective land allocation methods (Case Study: Chelgerd Watershed), Journal of Ecohydrology, 3(3), 363-377. [In Persian].
  13. Jereon, M., Anton, V.R. Tim, Q., Manuel, M., Christian P. and Dominique, A., 2013. Predicting future spatial distribution of SOC across entire France, Geophysical Research Abstracts, 15, p 1.
  14. Justesen, P.D., 2009. Multi‌–‌Objective optimization using evolutionary algorithms, Department of Computer Science, University of Denmark, Progress report, p 36.
  15. Lehmann, N., Briner, S. and Finger, R., 2013. The impact of climate and price risks on agricultural land use and cropmanagement decisions, Land Use Policy. 35, 119– 130.
  16. Li, X., 2007. A sustainable land allocation model with the integration of remote sensing and GIS a case study in Dongguan, Guangzhou institute of geography, p 26.
  17. Memmah, M.M., Lescourret, F., Yao, X. and Lavigne, C., 2015. Metaheuristics for agricultural land use optimization. A Review, p 24.
  18. Pajoohesh, M., Gorji, M., Taheri, M., Sarmadiyan, F., Mohamadi, j. and 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].
  19. Pishdad Salmanabad, L., Najafinejad A. and Salmanmahini, A., 2008. Survey the effects of land use change on soil erosion in Cheraghveise watershed. Journal of Agricultural Sciences and Natural Resources. 5(1), 141-149. [In Persian].
  20. Porta, J., Parapar, J., Doallo, R., Rivera, F.F., Sante, I. and Crecente. R., 2013. High performance genetic algorithm for land use planning, Computers, Environment and Urban Systems. 37, 45–58.
  21. Rockstrom, J. and Karlberg, L., 2010. Managing water in Rain-fed agriculture - the need for a paradigm shift. Agricultural Water Management. 97(4), 543–550.
  22. Sahnoun, H., Serbaji, M.M., Karray, B. and Medhioub, K., 2012. GIS and multicriteria analysis to select potential sites of agro-industrial complex, Environmental Earth Sciences. 66(8), 2477-2489.
  23. Salyani, T., 1996. Design the crop pattern in water resources projects. Agricultural Economics and Development. 4(15), 91-93. [In Persian].
  24. Shaygan, M., Alimohammadi, A., Mansourian, A., Shams Govara, Z. and 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.
  25. Terrence, J.T., George R.F. and Kenneth, G.R., 2001. Soil erosion, Jhon Wiley and sons, INK, USA, p 338.
  26. Yeo, I., Gorden S.I. and Guldmann, J.M., 2004. Optimizing patterns of land use to reduce peak runoff flow and nonpoint source pollution with an integrated hydrological and land-use model. Earth Interact. 8, 1-19.