2022
DOI: 10.46488/nept.2022.v21i05.008
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Assessment of Corrosion Potential Based on Water Quality Index in the Distribution Network of Urban Patna, Bihar, India

Abstract: Corrosion in the distribution network pipe can lead to pipe failure and water quality problems. This study assesses the corrosion or scaling potential based on the Water Quality Index (WQI) of drinking water in the distribution networks of Patna, Bihar, India. The water samples were collected from 18 points of the distribution network. In situ parameters like temperature, pH, electrical conductivity, and TDS were measured. Other parameters such as Alkalinity, Total hardness, Calcium, Magnesium, Chloride, Resid… Show more

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Cited by 7 publications
(2 citation statements)
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“…The coefficient of correlation r less than 0.5 means weakly related and r greater than 0.5 means strongly related. 25,26 The correlation between noise indices is generated using IBM SPSS Statistics version 28 (given in Table 5). There is a strong correlation between NPL with TNI and NC (r > 0.5).…”
Section: Correlations Of Noise Indicesmentioning
confidence: 99%
“…The coefficient of correlation r less than 0.5 means weakly related and r greater than 0.5 means strongly related. 25,26 The correlation between noise indices is generated using IBM SPSS Statistics version 28 (given in Table 5). There is a strong correlation between NPL with TNI and NC (r > 0.5).…”
Section: Correlations Of Noise Indicesmentioning
confidence: 99%
“…Reference [8] proposes a deep convolutional neural network fault area localization method based on transfer learning and verifies that this method can accurately locate the fault area even in small sample situations. Reference [9] compares the LSTM prediction results with extreme learning machines, backpropagation neural networks, and K-nearest neighbor regression, indicating that the use of LSTM networks can significantly reduce prediction errors. Reference [10] proposes a distribution network day ahead optimization scheduling strategy based on improved deep reinforcement learning, and verifies that it is more flexible and targeted compared to traditional two-stage stochastic programming methods.…”
Section: Introductionmentioning
confidence: 99%