Aadhaar Enabled Water Distribution System

D. Giridhar Reddy (), Darshan V (), N. S. Girish Rao Salanke (), Shobha G. () and Manas M.n ()
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D. Giridhar Reddy: RVCE
Darshan V: RVCE
N. S. Girish Rao Salanke: RVCE
Shobha G.: RVCE
Manas M.n: RVCE

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 7, No 2, 2279-2291

Abstract: Abstract Water Scarcity is a very severe problem across the world, one of the main factors is improper distribution of water and careless use of water by people, this is only going to be more severe in future as population and needs of the world rises. Many countries have increased deployment of smart water meters to monitor water usage and tried convincing people to not use water in a careless manner but have not been successful yet. This research paper presents the development and implementation of a smart water meter (SWM) prototype for household water consumption measurement. The SWM utilizes Wi-Fi or Long Range (LoRa) technology to transmit data and is integrated with Citizen Id (SSN) to centralize water distribution, and help detect water theft. Additionally, the meter incorporates SARIMA forecasting to predict water consumption based on past usage trends on the edge. The water consumption data can be accessed through a web and Android application, and an integrated billing system has been developed to provide users with information about their current water usage. The machine learning model was trained and tested on the water consumption dataset by DAIAD. The DAIAD dataset consists of hourly water consumption time series for 1,007 randomly selected consumers from the AMAEM (Association of Energy and Water Management) utility in Alicante, Spain, spanning from January 2015 to May 2017, totaling 16,857,056 measurements. The whole system was tested by installing it in a house and the forecasting model achieved an accuracy of 74%.

Keywords: Smart Water Meter; Aadhaar Enabled Service; IOT; Edge Computing; Data Analytics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03759-2

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