首页|New Machine Learning Data Have Been Reported by Researchers at Charles Darwin Un iversity (Leak detection and localization in underground water supply system usi ng thermal imaging and geophone signals through machine learning)
New Machine Learning Data Have Been Reported by Researchers at Charles Darwin Un iversity (Leak detection and localization in underground water supply system usi ng thermal imaging and geophone signals through machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Darwin, Austra lia, by NewsRx editors, research stated, “The underground water pipeline system is a crucial infrastructure that largely remains out of sight. However, it is th e source of a clean and uninterrupted flow of water for our everyday lives.” Our news editors obtained a quote from the research from Charles Darwin Universi ty: “Various factors, including corrosion, material degradation, ground movement , and improper maintenance, cause pipe leaks, a silent crisis that causes an est imated 39 billion dollars of loss every year. Prompt leakage detection and local ization can help reduce the loss. This research investigates the potential of tw o machine learning models as supporting tools for surveying extensive areas to i dentify and pinpoint the location of underground leaks. The presented combined a pproach ensures the speed and accuracy of the leakage survey. The first machine learning model is a hybrid ML model that employs thermal imaging to identify sub terranean water leakage. It relies on detecting thermal anomalies and distinctiv e signatures associated with water leakage to identify and locate underground wa ter leakage. The developed model can detect up to 750 mm underground leakage wit h 95.20 % accuracy. The second model uses binaural audio from geop hones to localize the leakage position.”
Charles Darwin UniversityDarwinAustr aliaAustralia and New ZealandCyborgsEmerging TechnologiesMachine Learnin g
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