Presenting an Optimal Model for Determining the Potential Areas of Industrial Development in Alborz Province

Document Type: Research Article


1 Department of Environment and PhD of Environmental Assessment

2 Retired Professor, Tehran Medical Sciences University, Faculty of Health

3 Department of Environmental Science, Faculty of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Remote Sensing and GIS, Graduate School of the Environment and Energy, Science and Research Branch, IAU, Tehran, Iran



In this study, three criteria and 10 sub-criteria and 18 indicators based on the ecological, economic and social characteristics of the Alborz province, while reviewing the internal and external resources, and using 30 expert opinions were sought in order to reach a collective consensus. Also to measure their weight, the FAHP fuzzy hierarchy process was used. Then, using weighted linear combination and geographic information system, the fuzzy desirability map of desirable arenas and their area were determined for industrial development of the province. Considering the highest accuracy and overlapping error of each of the models separately provides the best field for industrial development in the province. The results of this study shows that the integration of colonial competition and genetics meta-evolutionary algorithms for optimizing, due to the multiplicity of repetitions to achieve an optimal goal, while considering uncertainty, to provide an optimal model for locating potential and prone areas and prone to industrial development is very useful and it is possible to use it and taking into account the indigenous criteria of each province of the country, to prepare and use the optimal model of industrial development of each province of the country for decision making. The obtained results show that more than 66,000 hectares of research area has capability are based on the optimal compilation model presented for industrial development.


Ahadi, A., and Ghazanfari, R. F. (2012). Combined Fuzzy Group Multi Criteria Decision Making Method.

Ballı, S., & Korukoğlu, S. (2009). Operating system selection using fuzzy AHP and TOPSIS methods. Mathematical and Computational Applications, 14(2), 119-130.

Baranyi, P. (2004). TP model transformation as a way to LMI-based controller design. IEEE Transactions on Industrial Electronics, 51(2), 387-400.

Cardona, O. D. (2006). A system of indicators for disaster risk management in the Americas. Measuring vulnerability to natural hazards—Towards disaster resilient societies.

Chatterjee, D., & Mukherjee, B. (2010). Study of fuzzy-AHP model to search the criterion in the evaluation of the best technical institutions: a case study. International Journal of Engineering Science and Technology, 2(7), 2499-2510.

Chamodrakas, I., Batis, D., & Martakos, D. (2010). Supplier selection in electronic marketplaces using satisficing and fuzzy AHP. Expert Systems with Applications, 37(1), 490-498.

Drobne, S., & Lisec, A. (2009). Multi-attribute decision analysis in GIS: weighted linear combination and ordered weighted averaging. Informatica, 33(4).

Douvere, F. (2008). The importance of marine spatial planning in advancing ecosystem-based sea use management. Marine policy, 32(5), 762-771.

Dudukovic, M. P. (2009). Frontiers in reactor engineering. Science, 325(5941), 698-701.

Ercanoglu, M., & Temiz, F. A. (2011). Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environmental Earth Sciences, 64(4), 949-964.

Escavy, J. I., & Herrero, M. J. (2013). The use of location–allocation techniques for exploration targeting of high place-value industrial minerals: A market-based prospectively study of the Spanish gypsum resources. Ore Geology Reviews, 53, 504-516.

Esmaeili, A., Moore, F., Keshavarzi, B., Jaafarzadeh, N., & Kermani, M. (2014). A geochemical survey of heavy metals in agricultural and background soils of the Isfahan industrial zone, Iran. Catena, 121, 88-98.

Fernández, I., & Ruiz, M. C. (2009). Descriptive model and evaluation system to locate sustainable industrial areas. Journal of Cleaner Production, 17(1), 87-100.

Ghodsypour, S. H., & O''Brien, C. (1998). A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International journal of production economics, 56, 199-212.

Gu, W., Wu, Z., Bo, R., Liu, W., Zhou, G., Chen, W., & Wu, Z. (2014). Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review. International Journal of Electrical Power & Energy Systems, 54, 26-37.

Gupta, S., & Goldar, B. (2005). Do stock markets penalize environment-unfriendly behaviour? Evidence from India. Ecological economics, 52(1), 81-95.

Jafari, M., Chahouki, M. Z., Tavili, A., Azarnivand, H., & Amiri, G. Z. (2004). Effective environmental factors in the distribution of vegetation types in Poshtkouh rangelands of Yazd Province (Iran). Journal of Arid Environments, 56(4), 627-641.

Jahangiri, M., Ghaderi, R., Haghani, A., & Nematollahi, O. (2016). Finding the best locations for establishment of solar-wind power stations in Middle-East using GIS: A review. Renewable and Sustainable Energy Reviews, 66, 38-52.

Kanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. P. (2006). A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85(3-4), 347-366.

Keya, Z. Y., Faryadi, S., Yavari, A., Kamali, Y., & Shabani, A. A. (2016). Habitat Suitability & Connectivity of Alborz Wild Sheep in the East of Tehran, Iran. Open Journal of Ecology, 6(06), 325.

Makhdoom, Majid (2005). Foundation of Land Use Planning, Sixth Edition, Tehran University Publication, Tehran.

Moeinaddini, M., Khorasani, N., Danehkar, A., & Darvishsefat, A. A. (2010). Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj). Waste management, 30(5), 912-920.

Monavari, S. M., Omrani, G. A., Karbassi, A., & Raof, F. F. (2012). The effects of socioeconomic parameters on household solid-waste generation and composition in developing countries (a case study: Ahvaz, Iran). Environmental monitoring and assessment, 184(4), 1841-1846.

Natsios, A. S. (2005). The nine principles of reconstruction and development. Agency for International Development Washington DC. 

Nasrollahi, N., & Salehi, M. (2015). Performance enhancement of double skin facades in hot and dry climates using wind parameters. Renewable Energy, 83, 1-12.

Noroozi, J., Akhani, H., & Breckle, S. W. (2008). Biodiversity and phytogeography of the alpine flora of Iran. Biodiversity and Conservation, 17(3), 493-521.

Padash, A. (2017). Modeling of Environmental Impact Assessment Based on RIAM and TOPSIS for Desalination and Operating Units. Environmental Energy and Economic Research, 1(1), 75-88.

Park, S., Jeon, S., Kim, S., & Choi, C. (2011). Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landscape and urban planning, 99(2), 104-114.

Provotorov, V. Y., Korokin, M. V., Pokrovskii, M. V., Povetkin, S. V., Lazareva, G. A., Stepchenko, A. A., & Bystrova, N. A. (2016). Endothelio-and cardioprotective effects of vitamin В6 and folic acid in modelling methionine-induced hyperhomocysteinemia. Research result: pharmacology and clinical pharmacology, 2(1).

Puente, B. N., Kimura, W., Muralidhar, S. A., Moon, J., Amatruda, J. F., Phelps, K. L., ... & Santos, C. X. (2014). The oxygen-rich postnatal environment induces cardiomyocyte cell-cycle arrest through DNA damage response. Cell, 157(3), 565-579.

Önüt, S., Efendigil, T., & Kara, S. S. (2010). A combined fuzzy MCDM approach for selecting shopping center site: An example from Istanbul, Turkey. Expert Systems with Applications, 37(3), 1973-1980.

Özdağoğlu, A., & Özdağoğlu, G. (2007). Comparison of AHP and fuzzy AHP for the multi-criteria decision making processes with linguistic evaluations.

Queiruga-Dios, A., Hernández-Encinas, A., Visus-Ruiz, I., & del Rey, Á. M. (2010, June). A Virtual Collaborative Environment Helps University Students to Learn Maths. In International Conference on Enterprise Information Systems (pp. 600-606). Springer, Berlin, Heidelberg.

Rahman, M. A., Rusteberg, B., Gogu, R. C., Ferreira, J. L., & Sauter, M. (2012). A new spatial multi-criteria decision support tool for site selection for implementation of managed aquifer recharge. Journal of environmental management, 99, 61-75.

Sarvar, A. S., Asadi, A., & Kalantari, K. (2010). Investing Effect of Eshtehard’s Industrial City on Development of Surrounding Rural Area, Kraj.

Samari, D., Azadi, H., Zarafshani, K., Hosseininia, G., & Witlox, F. (2012). Determining appropriate forestry extension model: Application of AHP in the Zagros area, Iran. Forest policy and economics, 15, 91-97.

Scott, A. J., & Knott, M. (1974). A cluster analysis method for grouping means in the analysis of variance. Biometrics, 507-512.

Shahabi, H., Hashim, M., & Ahmad, B. B. (2015). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environmental Earth Sciences, 73(12), 8647-8668.

Stanney, K. M., Mollaghasemi, M., Reeves, L., Breaux, R., & Graeber, D. A. (2003). Usability engineering of virtual environments (VEs): identifying multiple criteria that drive effective VE system design. International Journal of Human-Computer Studies, 58(4), 447-481.

Soltani, A., & Galeshi, S. (2002). Importance of rapid canopy closure for wheat production in a temperate sub-humid environment: experimentation and simulation. Field Crops Research, 77(1), 17-30.

Soto, M. E. C. (2012). The identification and assessment of areas at risk of forest fire using fuzzy methodology. Applied Geography, 35(1-2), 199-207.

Torfi, F., Farahani, R. Z., & Rezapour, S. (2010). Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives. Applied Soft Computing, 10(2), 520-528.

Wang, Y. M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European journal of operational research, 186(2), 735-747.

Wang, X., Chan, H. K., Yee, R. W., & Diaz-Rainey, I. (2012). A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. International Journal of Production Economics, 135(2), 595-606.

Yang, X., Zheng, X. Q., & Chen, R. (2014). A land use change model: Integrating landscape pattern indexes and Markov-CA. Ecological Modelling, 283, 1-7.

Zarabadi, Z. S. S., & Khaliji, M. A. (2014). Evaluation of women’s role in development in development of Lorestan Province.