Mathematical Model for Detection of Leakage in Domestic Water Supply Systems by Reading Consumption from an Analogue Water Meter

TL;DR

This paper presents a mathematical model utilizing machine learning to detect water leakage in domestic systems by analyzing real-time consumption data from analogue meters, focusing on deviations and steady consumption patterns.

Contribution

It introduces a novel real-time leakage detection model based on machine learning applied to analogue water meters, with a custom device for data transfer.

Findings

Effective detection of leakage through consumption deviation analysis

Successful implementation of the model on a household water system

Development of a device for real-time data transfer from analogue meters

Abstract

In this article we introduce the principles to detect leakage using a mathematical model based on machine learning and domestic water consumption monitoring in real time. The model uses data which is measured from a water meter, analyzes the water consumption, and uses two criteria simultaneously: deviation from the average consumption, and comparison of steady water consumptions over a period of time. Simulation of the model on a regular household consumer was implemented on Antileaks - device that we have built that designed to transfer consumption information from an analogue water meter to a digital form in real time.