Using Intercriteria Analysis for Assessment of the Pollution Indexes of the Struma River
In this paper we are presenting the recently proposed approach Intercriteria Analysis (ICrA) for assessment of the pollution index of the Struma River in Bulgaria. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. At the first we have investigated all indexes at the all measurement point with ICrA and we have searched the dependences between points. Results show the measurement points are dependent criteria and we have ignored some over others. At the second we have applied the ICrA to establish the pollution relations and the model structure based on different criteria involved in the Struma River. The investigations show that there are three positive consonances and dissonances between criteria. Using of a Modification of the Time Series Analysis (MTSA) method we have developed an adequate mathematical model of the pollution dynamic as function of time.
KeywordsIntercriteria analysis Index matrices Intuitionistic fuzzy sets Pollution index Modelling Modification times series analysis Struma river
The authors are thankful for the support provided by the project DFNI-I-02-5/2014 “InterCriteria Analysis—New Approach for Decision Making”, funded by the National Science Fund, Bulgarian Ministry of Education and Science.
- 1.Ilkova, T.C., Petrov, M.M: Investigation of the pollution of the Struma River by application of a modified times series analysis method. Modelling and Prognosis. Ecology and Safety. International Scientific Publication 2, 495–506 (2008)Google Scholar
- 2.Ilkova, T.C., Petrov, M.M., Atanasova, M.P., Diadovski, I.K.: Modeling of the water pollution of the Struma River at the end of the Bulgarian part. J. Balkan Ecol. 9, 435–441 (2007)Google Scholar
- 3.Ilkova, T.S., Petrov M.M.: Choice of Adequate Model for Modelling, Analysis and As-sessment of Water River Ecosystems. Int. J. Bioautomation 9, 100–107 (2008)Google Scholar
- 4.Atanassov, K.T., Mavrov, D., Atanassova, V.K: Intercriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues Intuitionistic Fuzzy Sets Generalized Nets 11, 1–8 (2014)Google Scholar
- 5.Atanassov, K.T.: Generalized Index Matrices: Comptesrendus de l’AcademieBulgare des Sciences 11, 15–18 (1987)Google Scholar
- 10.Atanassova, V.K., Doukovska, L., Mavrov, D., Atanassov, K.T.: InterCriteria decision making approach to eu member states competitiveness analysis: temporal and threshold analysis. Proc. IEEE Int. Syst. 1, 97–106 (2014)Google Scholar
- 11.Atanassova, V.K., Doukovska, L., Atanassov, K.T., Mavrov, D.: Intercriteria decision making approach to EU member states competitiveness analysis. Proc. Int. Symp. Bus. Model. Soft. Des. 1, 289–294 (2014)Google Scholar
- 12.Atanassova, V.K., Vardeva, I: Sum- and average-based approach to criteria shortlisting in the InterCriteria analysis. Notes Intuitionistic Fuzzy Sets 20(4), 41–46 (2014)Google Scholar
- 13.Atanassova, V.K., Mavrov, D., Doukovska, L., Atanassov, K.T.: Discussion on the threshold values in the InterCriteria decision making approach. Notes Intuitionistic Fuzzy Sets 20(2), 94–99 (2014)Google Scholar
- 14.Ilkova, T.S., Petrov, M.M.: Application of InterCriteria analysis to the Mesta River pollution modelling. Notes Intuitionistic Fuzzy Sets 21(2), 118–125 (2015)Google Scholar
- 15.Lange, H.: Time-series Analysis in Ecology. Wiley (2001)Google Scholar
- 17.Stoyanov, S.: Optimization Methods and Algorithms. Technique, Sofia (1990) (in Bulgarian)Google Scholar