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A novel comprehensive framework for analyzing and assessing water quality and failure consequences based on Bayesian networks
Author(s) -
Zerouali Bilal,
Zerouali Bilel
Publication year - 2021
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.1002/wer.1450
Subject(s) - bayesian network , plan (archaeology) , computer science , quality (philosophy) , water quality , bayesian probability , risk analysis (engineering) , intervention (counseling) , machine learning , artificial intelligence , business , philosophy , psychology , ecology , archaeology , epistemology , psychiatry , biology , history
The study of the effects of mixing potable water with wastewater is a complex and difficult research area. This difficulty is because water and sewage networks are subject to various physical, environmental, and operational factors. The main objective of the study was to propose a new comprehensive framework for analyzing and assessing water quality based on Bayesian networks. An intervention plan was proposed to reduce the consequences of water quality and networks failure. The proposed framework was applied to water distribution network of Mdaourouch city (Souk Ahras, Algeria) to demonstrate its effectiveness. The results indicated that the water contamination rate has reached 33.9 %, which caused severe consequences. The effectiveness of the proposed plan has been verified theoretically using simulations, and the results have proven to be very satisfactory. The proposed model is a decision support tool, which is expected to assist decision‐makers and engineers in reviewing their plans and making the right decision. Practitioner Points This paper proposes a novel comprehensive framework for analyzing and assessing water quality and failure consequences based on Bayesian networks. This paper revisits the failure consequences. An intervention plan is proposed to reduce failure consequences. Results demonstrate that the proposed plan leads to fewer consequences probabilities. The proposed method can give the probability of failure of water and sewer network.