首页|New Machine Learning Study Findings Reported from University of Connecticut (Non -Parametric Machine Learning Modeling of Tree- Caused Power Outage Risk to Overhe ad Distribution Powerlines)
New Machine Learning Study Findings Reported from University of Connecticut (Non -Parametric Machine Learning Modeling of Tree- Caused Power Outage Risk to Overhe ad Distribution Powerlines)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from S torrs, Connecticut, by NewsRx correspondents, research stated, "Trees in proximi ty to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs." The news correspondents obtained a quote from the research from University of Co nnecticut: "This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM's performance using decision tree (DT), random forest (RF), k-Nearest Neigh bor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM ) techniques. The RF algorithm demonstrated the highest performance with an accu racy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693 . The SVM achieved the highest recall value of 0.727. Based on the overall perfo rmance, the RF emerged as the best machine learning algorithm, whereas the DT wa s the least suitable."
University of ConnecticutStorrsConne cticutUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSupport Vector Machines
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