Water Supply Management Through an Innovative Dashboard Solution

Ms. S. Ashwini; Ms. D. Santhiya

doi:10.32628/CSEIT24103215

Authors

Ms. S. Ashwini Chettinad College of Engineering and Technology, Karur, Tamil Nadu, India Author
Ms. D. Santhiya Chettinad College of Engineering and Technology, Karur, Tamil Nadu, India Author

DOI:

https://doi.org/10.32628/CSEIT24103215

Keywords:

Water Distribution Network, Logistic Regression, Vulnerability Analysis, Leak Detection, Machine Learning, Artificial Intelligence, Big Data Processing, GIS, Hydrological Modelling, Water Supply, Sewer Design, Agricultural Pollution, Nonpoint Source Pollution

Abstract

The increased public awareness and stronger restrictions have led to a rising recognition of the significance of advanced technology, especially Geographic Information Systems (GIS), in the management of water resources. The use of GIS in water management is highlighted in this work, with particular attention paid to pollution control, hydrologic modelling, and analytical water supply systems. Furthermore, by combining six conditioning factors and applying vulnerability analysis and logistic regression, it offers a novel approach to leak detection in water distribution networks (WDNs). In order to support preventive maintenance and resource allocation, the model predicts vulnerable locations properly. It is also suggested to use machine learning algorithms to optimise turbine maintenance schedules as part of a predictive maintenance framework for hydroelectric power facilities. High accuracy rates are attained by the framework, which finds important variables for maintenance prediction. These methods provide useful instruments for effective management of water resources overall.

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