首页|New Findings from Northeast Normal University Describe Advances in Machine Learn ing (Estimating Landslide Hazard Distribution Based On Machine Learning and Biva riate Statistics In Utmah Region,Yemen)
New Findings from Northeast Normal University Describe Advances in Machine Learn ing (Estimating Landslide Hazard Distribution Based On Machine Learning and Biva riate Statistics In Utmah Region,Yemen)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting originating from Changchun,People 's Republic of China,by NewsRx correspondents,research stated,"Landslides rep resent significant risks to human activity,leading to infrastructure damage and loss of life. This study focuses on assessing landslide hazards in Utmah Region ,Yemen." Financial support for this research came from Major Scientific and Technological Program of Jilin Province. Our news editors obtained a quote from the research from Northeast Normal Univer sity,"The evaluation involves comparing the effectiveness of the relative frequ ency ratio model with five machine learning algorithms (MLAs) for hazard mapping . Field surveys,high-resolution satellite imagery,and aerial photography were utilized in the study. The inventory map was generated after identifying and map ping 100 landslides. The inventory was then divided randomly into a training dat aset (70 landslides) and a validation dataset (30 landslides),with an equal num ber of non-landslide pixels. Eleven additional landslide conditioning factors we re collected from various sources,and the frequency ratio (FR) approach was emp loyed to identify the most crucial variables for modeling. The models were rigor ously tested and assessed using statistical metrics,including the Friedman and Wilcoxon signed-rank tests,as well as the area under the receiver operating cha racteristics (AUROC) curve. The findings based on the training and validation da tasets revealed that the RF algorithm (AUROC,0.992) outperformed the other mode ls in generating hazard maps. The XGBoost model (AUROC,0.991),NB model (AUROC,0.970),ANN model (AUROC,0.922),KNN model (AUROC,0.877),and FR (AUROC,0.67 4) were found to be less effective."
ChangchunPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningNortheast Normal Universi ty
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