首页|Researcher at University of Exeter Has Published New Study Findings on Machine L earning (An explainable machine learning approach to the prediction of pipe fail ure using minimum night flow)
Researcher at University of Exeter Has Published New Study Findings on Machine L earning (An explainable machine learning approach to the prediction of pipe fail ure using minimum night flow)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting out of Exeter, United Kingdo m, by NewsRx editors, research stated, “ABSTRACT: Both minimum night flow (MNF) and pipe failures are common ways of understanding leakage within water distribu tion networks (WDNs).” Funders for this research include South West Water. The news reporters obtained a quote from the research from University of Exeter: “This article takes a data-driven approach and applies linear models, random fo rests, and neural networks to MNF and pipe failure prediction. First, models are trained to estimate the historic average MNF for over 800 real-world DMAs from the UK. Features for this problem are constructed from pipe records which detail the length, diameter, volume, age, material, and number of customer connections of each pipe. The results show that 65% of the variation in histo ric average MNF can be explained using these factors alone. Second, a novel meth od is proposed to deconstruct the models’ predictions into a leakage contributio n score (LCS), estimating how each individual pipe in a DMA has contributed to t he MNF. In order to validate this novel approach, the LCS values are used to cla ssify pipes based on historic pipe failure and are compared against models direc tly trained for this.”
University of ExeterExeterUnited Kin gdomEuropeCyborgsEmerging TechnologiesMachine Learning
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