Evaluating water reuse applications under uncertainty: generalized intuitionistic fuzzy-based approach

Water reuse is a viable option to increase urban water supply, especially under new realities of climate change and increasing anthropogenic activities. A sustainable water reuse application should be cost-effective and have acceptable health risk to consumers. Water reuse application evaluation is complex because data acquisitions are usually associated with the problems of uncertainty, hesitancy, and parameterization. In this paper, a generalized intuitionistic fuzzy soft set (GIFSS)-based decision support framework is proposed to provide an effective approach to describe uncertainty and hesitancy in an intuitionistic fuzzy number. In addition, the modified measures of comparison and similarity are proposed to compare water reuse applications. Then, the proposed framework is applied to the City of Penticton (British Columbia, Canada) to evaluate seven water reuse applications. The evaluation results show that the applications of garden flower watering and public parks watering are the most preferred alternatives, which are consistent with the existing practice in the city. Furthermore, the results are highly affected by the generalized parameter and the weights of evaluation criteria. Both the comparison measure-based and similarity measure-based evaluations within the same GIFSS-based framework produce consistent results, indicating an applicable and efficient methodology.

[1]  E. Løken Use of multicriteria decision analysis methods for energy planning problems , 2007 .

[2]  Xinyang Deng,et al.  An improved method on group decision making based on interval-valued intuitionistic fuzzy prioritized operators , 2014 .

[3]  C. Bao,et al.  Water Resources Flows Related to Urbanization in China: Challenges and Perspectives for Water Management and Urban Development , 2011, Water Resources Management.

[4]  Zeshui Xu,et al.  Intuitionistic Fuzzy Aggregation Operators , 2007, IEEE Transactions on Fuzzy Systems.

[5]  Jikun Huang,et al.  Understanding the Water Crisis in Northern China: What the Government and Farmers are Doing , 2009 .

[6]  P. Knox,et al.  Urbanization: An Introduction to Urban Geography , 1993 .

[7]  J. Rose,et al.  Quantitative Microbial Risk Assessment , 1999 .

[8]  Jian Jhen Chen,et al.  Approach to Group Decision Making Based on Interval-Valued Intuitionistic Judgment Matrices , 2007 .

[9]  Krassimir T. Atanassov,et al.  Intuitionistic Fuzzy Sets - Theory and Applications , 1999, Studies in Fuzziness and Soft Computing.

[10]  Chao Zhou,et al.  Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP , 2013, Stochastic Environmental Research and Risk Assessment.

[11]  H. Tatli,et al.  Statistical complexity in daily precipitation of NCEP/NCAR reanalysis over the Mediterranean Basin , 2014 .

[12]  R. He,et al.  Runoff sensitivity to climate change for hydro-climatically different catchments in China , 2017, Stochastic Environmental Research and Risk Assessment.

[13]  Zhi Chen,et al.  A model-based fuzzy set-OWA approach for integrated air pollution risk assessment , 2014, Stochastic Environmental Research and Risk Assessment.

[14]  J. Diamond,et al.  China's environment in a globalizing world , 2005, Nature.

[15]  Ni-Bin Chang Sustainable water resources management under uncertainty , 2005 .

[16]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[17]  V. Lakshmana Gomathi Nayagam,et al.  Multi-criteria decision-making method based on interval-valued intuitionistic fuzzy sets , 2011, Expert Syst. Appl..

[18]  P. Mayer Residential End Uses of Water , 1999 .

[19]  Kaan Ozbay,et al.  Guidelines for Life Cycle Cost Analysis , 2003 .

[20]  H. Tatlı Detecting persistence of meteorological drought via the Hurst exponent , 2015 .

[21]  Stephen R. Petersen,et al.  Life-cycle costing manual for the Federal Energy Management Program , 1996 .

[22]  G. Wade Miller,et al.  Integrated concepts in water reuse: managing global water needs , 2006 .

[23]  Caroline A Sullivan,et al.  The Water Poverty Index: an International Comparison , 2002 .

[24]  Z. Xu,et al.  Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making , 2007 .

[25]  Kanad K. Biswas,et al.  Generalized intuitionistic fuzzy soft set and its application in practical medical diagnosis problem , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[26]  Yongxi Jiang,et al.  China's water scarcity. , 2009, Journal of environmental management.

[27]  K. Benke,et al.  Quantitative microbial risk assessment: uncertainty and measures of central tendency for skewed distributions , 2008 .

[28]  T. Saaty How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[29]  Yong Tang,et al.  An adjustable approach to intuitionistic fuzzy soft sets based decision making , 2011 .

[30]  Zeshui Xu,et al.  Some geometric aggregation operators based on intuitionistic fuzzy sets , 2006, Int. J. Gen. Syst..

[31]  Karl Schaefer,et al.  Water Reuse and Recycling in Canada: A Status and Needs Assessment , 2004 .

[32]  Pabitra Kumar Maji,et al.  More on Intuitionistic Fuzzy Soft Sets , 2009, RSFDGrC.

[33]  Solomon Tesfamariam,et al.  Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP) , 2009 .

[34]  Manish Agarwal,et al.  Generalized intuitionistic fuzzy soft sets with applications in decision-making , 2013, Appl. Soft Comput..

[35]  D. Dupont Water use restrictions or wastewater recycling? A Canadian willingness to pay study for reclaimed wastewater , 2013 .

[36]  Huu Hao Ngo,et al.  A critical review on sustainability assessment of recycled water schemes. , 2012, The Science of the total environment.

[37]  Barry T. Hart,et al.  A Bayesian network approach to support environmental flow restoration decisions in the Yarra River, Australia , 2013, Stochastic Environmental Research and Risk Assessment.

[38]  Massoud Tabesh,et al.  Integrated risk assessment of urban water supply systems from source to tap , 2013, Stochastic Environmental Research and Risk Assessment.

[39]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[40]  Patrick T. Hester,et al.  An Analysis of Multi-Criteria Decision Making Methods , 2013 .

[41]  Huawen Liu,et al.  Multi-criteria decision-making methods based on intuitionistic fuzzy sets , 2007, Eur. J. Oper. Res..

[42]  Rehan Sadiq,et al.  Clostridium difficile infection incidence prediction in hospitals (CDIIPH): a predictive model based on decision tree and fuzzy techniques , 2017, Stochastic Environmental Research and Risk Assessment.

[43]  Huayou Chen,et al.  Continuous interval-valued intuitionistic fuzzy aggregation operators and their applications to group decision making , 2014 .

[44]  M. Zarghami,et al.  Stochastic-fuzzy multi criteria decision making for robust water resources management , 2009 .

[45]  Fuzhan Nasiri,et al.  A system dynamics approach for urban water reuse planning: a case study from the Great Lakes region , 2013, Stochastic Environmental Research and Risk Assessment.

[46]  D. Satterthwaite The implications of population growth and urbanization for climate change , 2009 .

[47]  Yun Peng,et al.  A Bayesian network based framework for multi-criteria decision making , 2004 .

[48]  D. Molodtsov Soft set theory—First results , 1999 .