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Application of Lightning Monitoring and Positioning Data in Investigation and Identification of Lightning Disaster in Inner Mongolia 被引量:1
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作者 Xuyu Feng Xiaodong Liu 《Meteorological and Environmental Research》 CAS 2013年第8期28-31,共4页
The lightning current magnitude and other characteristics are important basic data of the lightning disaster investigation and identification. The characteristics of lightning monitoring and positioning system in Inne... The lightning current magnitude and other characteristics are important basic data of the lightning disaster investigation and identification. The characteristics of lightning monitoring and positioning system in Inner Mongolia were introduced and studied in the lightning accident analysis based on the lightning monitoring and positioning data of the lightning stroke accidents. The positioning error of lightning monitoring and positioning system was analyzed. The results showed that lightning current intensity and the position precision were very important data in the lightning disaster investigation. Finally, a variety of meteorological data should be applied in the lightning disaster investigation and identification. 展开更多
关键词 Ughtning monitoring and positioning data Lightning disaster investigation and identification Positioning error China
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Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China
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作者 Ao Zhang Xin-wen Zhao +8 位作者 Xing-yuezi Zhao Xiao-zhan Zheng Min Zeng Xuan Huang Pan Wu Tuo Jiang Shi-chang Wang Jun He Yi-yong Li 《China Geology》 CAS CSCD 2024年第1期104-115,共12页
Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Co... Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems. 展开更多
关键词 Landslides susceptibility assessment Machine learning Logistic Regression Random Forest Support Vector Machines XGBoost Assessment model Geological disaster investigation and prevention engineering
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