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.
Water quality Neural networks Fuzzy logic Neuro-fuzzy
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