NDLI: Application of neuro-fuzzy based expert system in water quality assessment

Content Provider	Springer Nature Link
Author	Aghaarabi, E. Aminravan, F. Sadiq, R. Hoorfar, M. Rodriguez, M. J. Najjaran, H.
Copyright Year	2014
Abstract	In this research a framework is developed to predict the drinking water quality through the neural network models. A fuzzy rule-based system and similarity measure algorithm yield a water quality index for different sampling locations in a water distribution network (WDN), and a neural network is trained using the quality indices. Different sources of uncertainty exist in this model, including deficient, missing, and noisy data, conflicting water quality parameters and subjective information. Hourly and monthly data from Quebec City WDN are used to illustrate the performance of the proposed neuro-fuzzy model. Also, historical data from 52 sampling locations of Quebec City network is utilized to develop the rule-based model in order to train the neural network. Water quality is evaluated by categorizing quality parameters in two groups including microbial and physicochemical. Two sets of rules are defined using expert knowledge to assign water quality grades to each sampling location in the WDN. The fuzzy inference system outputs are deffuzzified using a similarity measure algorithm in this approach. The fuzzy inference system acts as a decision making agent. A utility function provides microbial and physicochemical water quality indices. Final results are used to train the neural network. In the proposed framework, microbial and physicochemical quality of water are predicted individually.
Starting Page	2137
Ending Page	2145
Page Count	9
File Format	PDF
ISSN	09756809
Journal	International Journal of System Assurance Engineering and Management
Volume Number	8
Issue Number	4
e-ISSN	09764348
Language	English
Publisher	Springer India
Publisher Date	2014-12-07
Publisher Place	New Delhi
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Water quality Neural networks Fuzzy logic Neuro-fuzzy Quality Control, Reliability, Safety and Risk Engineering Economics, Organization, Logistics, Marketing
Content Type	Text
Resource Type	Article
Subject	Strategy and Management Safety, Risk, Reliability and Quality

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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