Abstract
The processing of monitored data is the key to water quality assessment and plays an important role in the development and application of water quality assessment models. Base on fuzzy random variable, an approach for processing the monitored data of water quality is proposed in this paper. In which, all monitored data information of water quality are included into a fuzzy random variable via an interval and a probability density function. Embedded into a one-factor assessment model, the proposed approach is applied to assess water quality in Chaohu lake, located in eastern China, to verify its feasibility. The results show that the approach proposed in the research has the advantage of reducing the loss of water quality information.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Horton, R.K.: An index number system for rating water quality. J. Water Pollut. Control. Fed. 37, 300–306 (1965)
Akomeah, E., Chun, K., Lindenschmidt, K.E.: Dynamic water quality modelling and uncertainty analysis of phytoplankton and nutrient cycles for the upper South Saskatchewan River. Environ. Sci. Pollut. Res. 22(22), 18239–18251 (2015)
Mohseni-Bandpei, A., Motesaddi, S., Eslamizadeh, M., et al.: Water quality assessment of the most important dam (Latyan dam) in Tehran, Iran. Environ. Sci. Pollut. Res. 25(29), 29227–29239 (2018)
Li, K., He, C., Zhuang, J., et al.: A long-term changes in the water quality and macroinvertebrate communities of a subtropical river in South China. Water 7, 63–80 (2015)
Ni, J., Xu, J., Zhang, M.: Incorporating pollutants interaction with the environment and parameter uncertainty in water quality evaluation:a case of Lake Chauhan, China. Water Sci. Technol. Water Supply 18(2), 723–736 (2018)
Canfield, D.E., Hoyer, M.V., Bachmann, R.W., et al.: Water quality changes at an outstanding Florida water: influence of stochastic events and climate variability. Lake Reserv. Manag. 32(3), 297–313 (2016)
Yan, F., Qian, B., Liu, C.: Introducing biological indicators into CCME WQI using variable fuzzy set method. Water Resour. Manag. 32(8), 2901–2915 (2018)
Kwakernaak, H.: Fuzzy random variables, part I: definitions and theorems. Inf. Sci. 15, 1–29 (1978)
Puri, M.L., Ralescu, D.A.: Fuzzy random variables. J. Math. Anal. Appl. 114, 409–422 (1986)
Kruse, R., Meyer, K.D.: Statistics with Vague Data. Reidel Publishing, Dordrecht (1987)
Gil, M.A., Lopez-Diaz, M., Ralescu, D.A.: Overview on the development of fuzzy random variables. Fuzzy Set Syst. 157, 2546–2557 (2006)
Chang, H., Yao, J., Ouyang, L.: Fuzzy mixture inventory model involving fuzzy random variable lead time demand and fuzzy total demand. Eur. J. Oper. Res. 169(1), 65–80 (2006)
Efendia, R., Arbaiy, N., Deris, M.M.: A new procedure in stock market forecasting based on fuzzy random auto-regression time series model. Inf. Sci. 441, 113–132 (2018)
Li, J., Xu, J., Gen, M.: A class of multiobjective linear programming model with fuzzy random coefficients. Math Comput. Model. 44, 1097–1113 (2006)
Shrestha, S., Kazama, F.: Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ. Model. Softw. 22, 464–475 (2007)
Liu, L., Zhou, J., An, X., et al.: Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China. Expert Syst. Appl. 37, 2517–2521 (2010)
Wang, W., Xu, D., Chau, K., et al.: Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resour. Manag. 28, 4183–4200 (2014)
Acknowledgments
The research is supported by the Quality Engineering Foundation of Hefei University (Grant No. 2017jyxm016), also supported by the Social Science Foundation of Anhui Province (Grant No. AHSKY2017D83),the Talent Foundation of Hefei University (Grant No. 16-17RC27),the Major Program for Humanities and Social Sciences of Education Bureau of Anhui Province (Grant No. SK2018ZD048). The authors thank the reviewers’ valuable comments and editors’ constructive suggestions, which will greatly improve the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, S., Zhang, L., Ni, J. (2020). An Approach for Processing the Real-time Monitoring Data of Water Quality and Its Application in Water Quality Evaluating. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-21248-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-21248-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21247-6
Online ISBN: 978-3-030-21248-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)