Document Type : CASE STUDY


Graduate Faculty of Environment, University of Tehran, Tehran, Iran


Environmental planning and management can have positive effects on development of some land uses including industrial areas that have a major effect on economic, social and environmental conditions. Considering the most important problems associated with modeling, the fundamental methods and functions of site-selection laid inside the geographical information system are not accounted for the multi-purpose experimental programs. The main purpose of this study is to present a systematic pattern for environmental management using genetic algorithm and fuzzy analytic hierarchy process in geographical information system in order to reduce uncertainty. Through fuzzy analytic hierarchy process, the weight of criteria was calculated after extracting the criteria by Delphi technique and identifying all the effective criteria and factors involved in site selection. After preparation of intended layers, each map was prepared in the form of raster layers on geographical information system. Information layers were combined after being valued and finally the map of suitable areas was prepared. Finally, the conformity of all the obtained maps was checked out with field conditions. In this study, the genetic algorithm was used as an optimization method applied for natural selection. It was also attempted to find better solutions among others. The results showed the best site for developing industries.

Graphical Abstract

An integrated environmental management approach to industrial site selection by genetic algorithm and fuzzy analytic hierarchy process in geographical information system


  • Based on the map of fuzzy AHP, about 23.2 % of the total area of Markazi province is very much suitable for the development of related industries
  • Based on the map of genetic algorithm, 25 locations are also suitable for the development of related industries
  • The genetic algorithm method is very convenient for finding optimal locations with the highest value, but this algorithm can be used for optimal points.  


Al-Mulali, U.; Weng-Wai, C.; Sheau-Ting, L.; Mohammed, A.H. (2015). Investigating the environmental Kuznets curve (EKC) hypothesis by utilizing the ecological footprint as an indicator of environmental degradation. Ecol. Indic., 48: 315-323 (9 pages).

Arabsheibani, R.; Kanani Sadat, Y.; Abedini, A. (2016). Land suitability assessment for locating industrial parks: a hybrid multi criteria decision‐making approach using Geographical Information System. Geogr . Res., 54: 446-460 (15 pages).

Aung, T.S., (2017). Evaluation of the environmental impact assessment system and implementation in Myanmar: Its significance in oil and gas industry. Environ. Impact Assess. Rev., 66: 24-32 (9 pages).

Blankendaal, T.; Schuur, P.; Voordijk, H., (2014). Reducing the environmental impact of concrete and asphalt: a scenario approach. J. Cleaner Prod., 66: 27-36 (10 pages).

Cao, Y.; Wu, Q., (1999). Teaching genetic algorithm using MATLAB. Int. J. Electr. Eng. Educ., 36: 139-153 (15 pages).

Chehreghan, A.; Rajabi, M.; Pazoki, S.H., (2013). Developing a novel method for optimum site selection based on fuzzy genetic system and GIS. Ijarcsse, 3: 165-174 (10 pages).

Corti, A.;Senatore, A., (2000). Project of an air quality monitoring network for industrial site in Italy. Environ. Monit. Assess., 65: 109-117 (9 pages).

Crossland, A.; Jones, D.; Wade, N., (2014). Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing. Int. J. Electr. Power Energy Syst., 59: 103-110 (8 pages).

Dengiz, O.; Ozcan, H.; Koksal, E.S.; Baskan, O.; Kosker, Y., (2010). Sustainable natural resource management and environmental assessment in the Salt Lake (Tuz Golu) Specially Protected Area. Environ. Monit. Assess., 161: 327-342 (16 pages).

Dey, P.K., (2006). Integrated project evaluation and selection using multiple-attribute decision-making technique. Int. J. Prod. Econ., 103: 90-103 (14 pages).

Eldrandaly, K.; Eldin, N.; Sui, D., (2003). A COM-based spatial decision support system for industrial site selection. J. Geogr. Inf. Dec. Anal., 7: 72-92 (21 pages).

Fischer, T.B.; Jha-Thakur, U.; Hayes, S., (2015). Environmental impact assessment and strategic environmental assessment research in the UK. J.  Environ. Assess. Polic. Manage., 17: 150-169 (20 pages).

Francis, A., (2015). Analyzing the environmental impact assessment process for sustainable development of the oil and gas industry in trinidad and tobago. Electrical thesis dessertation. (106 pages).

Houck, C.R.; Joines, J.; Kay, M.G., (1995). A genetic algorithm for function optimization: a Matlab implementation. Ncsu. Ie. Tr., 95: 1-10 (10 pages).

Jamshidi Zanjani, A.; Rezaei, M., (2017). Landfill site selection using combination of fuzzy logic and multi-attribute decision-making approach. Environ. Ear. Sci. 76: 448-456 (9 pages).

Lin, B.; Wang, X., (2015). Carbon emissions from energy intensive industry in China: evidence from the iron & steel industry. Renewable Sustainable Energy Rev., 47: 746-754 (9 pages).

Motevali, A.; Koloor, R.T., (2017). A comparison between pollutants and greenhouse gas emissions from operation of different dryers based on energy consumption of power plants. J. Cleaner Prod., 154: 445-461 (17 pages).

Nabernegg, S.; Bednar-Friedl, B.; Wagner, F.; Schinko, T.; Cofala, J.; Clement, Y.M., (2017). The deployment of low carbon technologies in energy intensive industries: A macroeconomic analysis for europe, Chi. In. Ener., 10: 360-371 (12 pages).

Noorollahi, Y.; Yousefi, H.; Mohammadi, M., (2016). Multi-criteria decision support system for wind farm site selection using GIS. Sustainable Energy Technol. Assess., 13: 38-50 (13 pages).

Norgate, T.; Jahanshahi, S.; Rankin, W., (2007). Assessing the environmental impact of metal production processes. J. Cleaner Prod., 15: 838-848 (11 pages).

Ozcan, H.; Cetin, M.; Diker, K., (2003). Monitoring and assessment of land use status by GIS. Environ. Monit. Assess., 87:33-45 (13 pages).

Peng, B.; Li, L., (2015). An improved localization algorithm based on genetic algorithm in wireless sensor networks. Cog. Neuro., 9: 249-256 (8 pages).

Rajabi, A.M.; Ghorbani, E., (2016). Land subsidence due to groundwater withdrawal in Arak plain, Markazi province, Iran. Arabian J. Geosci., 9: 738-751 (14 pages).

Razif, M.; Persada, S.F., (2016). Environmental impact assessment framework for ekolabel certification initiative in indonesia: Case study of a rattan-plywood based furniture industry. Int. J. Chem.Tech Res., 9: 634-643 (10 pages).

Reisi, M.; Aye, L.; Soffianian, A., (2011). Industrial site selection by GIS in Isfahan, Iran.  Geoinformatics, IEEE., 19: 24-36 (13 pages).

Rikalovic, A.; Cosic, I.; Labati, R.D.; Piuri, V., (2015). A comprehensive method for industrial site selection: the macro-location analysis. IEEE Syst. J., 7: 971-980 (10 pages).

Rikalovic, A.; Cosic, I.; Labati, R.D.; Piuri, V., (2017). Intelligent decision support system for industrial site classification: a gis-based hierarchical neuro-fuzzy approach. IEEE Syst. J., 10: 1-12 (12 pages).

Rikalovic, A.; Cosic, I.; Lazarevic, D., (2014). GIS based multi-criteria analysis for industrial site selection. Procedia Eng., 69: 1054-1063 (10 pages).

Rikhtegar, N.; Mansouri, N.; Ahadi Oroumieh, A.; Yazdani-Chamzini, A.; Kazimieras Zavadskas, E.; Kildienė, S., (2014). Environmental impact assessment based on group decision-making methods in mining projects. Eco. Res., 27: 378-392 (15 pages).

Tu, F.; Yu, X.; Ruan, J., (2014). Industrial land use efficiency under government intervention: Evidence from Hangzhou, China. Hab. Int., 43: 1-10 (10 pages).

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