首页|Findings from Chinese Academy of Sciences Provide New Insights into Machine Lear ning (Integrating Machine Learning Ensembles for Landslide Susceptibility Mappin g in Northern Pakistan)
Findings from Chinese Academy of Sciences Provide New Insights into Machine Lear ning (Integrating Machine Learning Ensembles for Landslide Susceptibility Mappin g in Northern Pakistan)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of Wuhan, People's R epublic of China, by NewsRx editors, research stated, "Natural disasters, notabl y landslides, pose significant threats to communities and infrastructure." Financial supporters for this research include Prince Sattam Bin Abdulaziz Unive rsity. The news journalists obtained a quote from the research from Chinese Academy of Sciences: "Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers t he northern region of Pakistan, which is primarily susceptible to landslides ami d rugged topography, frequent seismic events, and seasonal rainfall, to carry ou t LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (R F), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a r andom forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological feature s, and proximity to faults, roads, and streams, and slope was revealed as the pr imary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and po tency of LSM."
Chinese Academy of SciencesWuhanPeop le's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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