首页|Findings from Lulea University of Technology Update Knowledge of Machine Learnin g (Identifying Climate-related Failures In Railway Infrastructure Using Machine Learning)
Findings from Lulea University of Technology Update Knowledge of Machine Learnin g (Identifying Climate-related Failures In Railway Infrastructure Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting from Lulea, Sweden, by NewsR x journalists, research stated, "Climate change impacts pose challenges to a dep endable operation of railway infrastructure assets, thus necessitating understan ding and mitigating its effects. This study proposes a machine learning framewor k to distinguish between climatic and non-climatic failures in railway infrastru cture." Funders for this research include Swedish Research Council Formas, Kempe Foundat ion. The news correspondents obtained a quote from the research from the Lulea Univer sity of Technology, "The maintenance data of turnout assets from Sweden's railwa y were collected and integrated with asset design, geographical and meteorologic al parameters. Various machine learning algorithms were employed to classify fai lures across multiple time horizons. The Random Forest model demonstrated a high accuracy of 0.827 and stable F1-scores across all time horizons. The study iden tified minimum-temperature and quantity of snow and rain prior to the event as t he most influential factors. The 24-hour time horizon prior to failure emerged a s the most effective time window for the classification."
LuleaSwedenEuropeCyborgsEmerging TechnologiesMachine LearningLulea University of Technology
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