1-D convolution neural network based leak detection, location and size estimation in smart water grid

IF 1.6 3区环境科学与生态学 Q3 WATER RESOURCES Urban Water Journal Pub Date : 2023-01-31 DOI:10.1080/1573062X.2022.2164732

Pooja Choudhary, B. Botre, S. A. Akbar

引用次数: 1

Abstract

ABSTRACT Water is one of the essential natural resources for survival, but the water transportation system faces significant challenges because of huge water loss due to leaky pipeline systems. An IoT based novel SWG prototype has been developed and reported in this work. The SWG comprises sensors and devices that can continuously and remotely monitor the pressure, temperature, flow, pH, turbidity, etc., of the water being transported. Moreover, a novel 1-D CNN model has been developed by creating an artificial leak on the pipeline that takes input data points as a chunk of 5-minute time series to the network and gives output in leak detection, location and size estimation simultaneously. Further, the developed model is compared with other state of the art machine learning techniques and the proposed model is found better in terms of accuracy which is 94.32%, 91.91% and 89.85% for leak detection, size estimation and location respectively.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于一维卷积神经网络的智能电网泄漏检测、定位和尺寸估计

摘要水是人类赖以生存的重要自然资源之一，但由于管道系统的漏水，造成了巨大的水损失，因此水运系统面临着巨大的挑战。一种基于物联网的新型SWG原型已在本工作中开发并报告。SWG由传感器和设备组成，可以连续和远程监测所输送的水的压力、温度、流量、pH值、浊度等。此外，通过在管道上创建人工泄漏，开发了一种新的1-D CNN模型，该模型将输入数据点作为5分钟时间序列的块输入网络，并同时给出泄漏检测、位置和大小估计的输出。此外，将所开发的模型与其他最先进的机器学习技术进行比较，发现所提出的模型在泄漏检测，大小估计和位置方面的准确率分别为94.32%，91.91%和89.85%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Urban Water Journal WATER RESOURCES-

CiteScore

4.40

自引率

11.10%

发文量

101

审稿时长

3 months

期刊介绍： Urban Water Journal provides a forum for the research and professional communities dealing with water systems in the urban environment, directly contributing to the furtherance of sustainable development. Particular emphasis is placed on the analysis of interrelationships and interactions between the individual water systems, urban water bodies and the wider environment. The Journal encourages the adoption of an integrated approach, and system''s thinking to solve the numerous problems associated with sustainable urban water management. Urban Water Journal focuses on the water-related infrastructure in the city: namely potable water supply, treatment and distribution; wastewater collection, treatment and management, and environmental return; storm drainage and urban flood management. Specific topics of interest include: network design, optimisation, management, operation and rehabilitation; novel treatment processes for water and wastewater, resource recovery, treatment plant design and optimisation as well as treatment plants as part of the integrated urban water system; demand management and water efficiency, water recycling and source control; stormwater management, urban flood risk quantification and management; monitoring, utilisation and management of urban water bodies including groundwater; water-sensitive planning and design (including analysis of interactions of the urban water cycle with city planning and green infrastructure); resilience of the urban water system, long term scenarios to manage uncertainty, system stress testing; data needs, smart metering and sensors, advanced data analytics for knowledge discovery, quantification and management of uncertainty, smart technologies for urban water systems; decision-support and informatic tools;...