SNDT WOMEN'S UNIVERSITY
BMK Knowledge Resource Centre
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Prediction of Reliability for Water Distribution System Using ANN and Benchmark Table
Suja S. Nair
Prediction of Reliability for Water Distribution System Using ANN and Benchmark Table - p649–664
This study employs artificial neural networks (ANN) and benchmark tables to forecast the water distribution system reliability in the Panchayats of Elamkunnapuzha, Njarakkal, and Nayarambalam (Zone IIB) in Cochin, Kerala. The research uses water usage data collected from 90 Kerala Water Authority (KWA) customers over 7 months (December 2022 to June 2023). A questionnaire-based survey was conducted with these customers to gather insights on the network's performance, validating the accuracy of the predictions generated by the ANN model. The study identifies several factors impacting the performance of the water distribution network, including system capacity, seasonal fluctuations, and customer usage patterns. The ANN model is employed to predict water demand from 2024 to 2034, assessing the reliability of the gridiron network in meeting future demands. The results of this study provide valuable insights into the functionality of the current network and highlight necessary adjustments to ensure continued reliability in the face of growing water demand in the region.
Building Information Modeling
Chemical Process Reliability
Drought
Smart Infrastructure
Water Industry and Water Technology
Water Policy
Prediction of Reliability for Water Distribution System Using ANN and Benchmark Table - p649–664
This study employs artificial neural networks (ANN) and benchmark tables to forecast the water distribution system reliability in the Panchayats of Elamkunnapuzha, Njarakkal, and Nayarambalam (Zone IIB) in Cochin, Kerala. The research uses water usage data collected from 90 Kerala Water Authority (KWA) customers over 7 months (December 2022 to June 2023). A questionnaire-based survey was conducted with these customers to gather insights on the network's performance, validating the accuracy of the predictions generated by the ANN model. The study identifies several factors impacting the performance of the water distribution network, including system capacity, seasonal fluctuations, and customer usage patterns. The ANN model is employed to predict water demand from 2024 to 2034, assessing the reliability of the gridiron network in meeting future demands. The results of this study provide valuable insights into the functionality of the current network and highlight necessary adjustments to ensure continued reliability in the face of growing water demand in the region.
Building Information Modeling
Chemical Process Reliability
Drought
Smart Infrastructure
Water Industry and Water Technology
Water Policy