Abstract
Currently, advanced methods have been developed to select an appropriate site for an engineering project. The ability to make a good decision in site selection can help the engineers to reduce the expensive costs, which are very important in large construction projects. In this paper, a new approach for site selection is presented. This method is based on rough set theory which is a mathematical theory presented by professor Pawlak. In this study, the results of the rough set decision-making are compared with the results of the regression method in a practical case study for the site location of a water treatment plant in Ardabil Province in the northwest of Iran, to demonstrate that the rough set theory provides a useful method for site selection. The results of practical studies indicate that using this method for site selection decision-making can reduce costs and prevent hazards that may happen due to civil engineering uncertainties.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abastante, F., Bottero, M., Greco, S., & Lami, I. M. (2014). Addressing the location of undesirable facilities through the dominance based rough set approach. Journal of Multi-Criteria Decision Analysis, 21(3), 3–23.
Arabani, M., & Amani, B. (2009). Evaluating the parameters affecting urban trip-generation. Iranian Journal of Science & Technology, Transaction B, Engineering, 31(B5), 547–560.
Arabani, M., & Lashteh Neshaei, M. A. (2006). Application of rough set theory as a new approach to simplify dams location. Scientia Iranica, 13(2), 152–158.
Arabani, M., Haghi, A. K., & Amani, B. (2009). Making a decision between the rehabilitation and reconstruction of asphalt pavements using the rough-set theory. Scientia Iranica, Transaction A: Civil Engineering, 16(2), 116–125.
Barbagallo, S., Consoli, S., Pappalardo, N., Greco, S., & Zimbone, S. M. (2006). Discovering reservoir operating rules by a rough set approach. Water Resources Management, 20, 19–36.
Chattefuee, S., Hadi, A.S. (2006). Regression analysis by example, 4th edition, John Wiley & Sons.
Chermisinoff, N. P. (2002). Handbook of water and wastewater treatment technologies. USA: Butterworth-Heinemann.
Gorsevski, P.V., Jankowski, P. (2008). Discerning landslide susceptibility using rough sets. Computers Environment and Urban Systems (32):53–65.
Greco, S., Matarazzo, B., & Słowiński, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129, 1–47.
Jiye Li (2007). Rough set based rule evaluations and their applications, PhD thesis, University of Waterloo.
Karami, J., Alimohammadi, A., & Seifouri, T. (2014). Water quality analysis using a variable consistency dominance-based rough set approach. Computers, Environment and Urban Systems, 43, 25–33.
Lashteh Neshaei, M. A., & Pirouz, M. (2010). Rough sets theory in site selection decision making for water reservoirs. Computational Methods in Civil Engineering, 1(1), 85–94.
Li, P., Wu, J. H., & Qian, H. (2012). Groundwater quality assessment based on rough sets attribute reduction and TOPSIS method in a semi-arid area, China. Environmental Monitoring and Assessment, 184(8), 4841–4854.
Liu, J. P., Zeng, Z. P., Liu, H. Q., & Wang, H. B. (2011). A rough set approach to analyze factors affecting landslide incidence. Computers & Geosciences, 37, 1311–1317.
Lu, Z., Qin, Z., Zhang, Y. C., & Fang, J. (2014). A fast feature selection approach based on rough set boundary regions. Pattern Recognition Letters, 36, 81–88.
Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341–356.
Pawlak, Z. (1991). Rough sets: theoretical aspects of reasoning about data. Dordrecht, Boston: Kluwer Academic Publishers.
Peng, L., Niu, R., Huang, B., Wua, X., Zhao, Y., & Ye, R. (2014). Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges area, China. Geomorphology, 204, 287–301.
Phophalia, A., & Mitra, S. K. (2015). Rough set based bilateral filter design for denoising brain MR images. Applied Soft Computing Journal. doi:10.1016/j.asoc.2015.04.005.
Province Water and Wastewater Company of Ardabil (2006). Roohkandi water treatment plant, the feasibility study report. Iran Ministry of Energy.
Slowinski, R., Greco, S., & Matarazzo, B. (2009). Rough sets in decision making. In R. A. Meyers (Ed.), Encyclopedia of complexity and systems science (pp. 7753–7786). New York: Springer.
US Army Corps of Engineers (2001). Engineering and design geotechnical investigations
Yesilnacar, M. I., & Cetin, H. (2005). Site selection for hazardous wastes: a case study from the GAP area, Turkey. Engineering Geology, 81, 371–388.
Yesilnacar, M. I., Süzen, M. L., Kaya, B. S., & Doyuran, V. (2012). Municipal solid waste landfill site selection for the city of Şanliurfa-Turkey: an example using MCDA integrated with GIS. International Journal of Digital Earth, 5(2), 147–164.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Arabani, M., Pirouz, M. Water treatment plant site location using rough set theory. Environ Monit Assess 188, 552 (2016). https://doi.org/10.1007/s10661-016-5539-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-016-5539-1