This study examines how socio-economic characteristics predict flood risk in London,England,using machine learning algorithms.The socio-economic variables considered included race,employment,crime and poverty measures...This study examines how socio-economic characteristics predict flood risk in London,England,using machine learning algorithms.The socio-economic variables considered included race,employment,crime and poverty measures.A stacked generalization(SG)model combines randomforest(RF),support vector machine(SVM),and XGBoost.Binary classification issues employ RF as the basis model and SVM as the meta-model.In multiclass classification problems,RF and SVM are base models while XGBoost is meta-model.The study utilizes flood risk labels for London areas and census data to train these models.This study found that SVM performs well in binary classifications with an accuracy rate of 0.60 and an area under the curve of 0.62.XGBoost outperforms other multiclass classification methods with 0.62 accuracy.Multiclass algorithms may perform similarly to binary classification jobs due to reduced data complexity when combining classes.The statistical significance of the result underscores their robustness,respectively.The findings reveal a significant correlation between flood risk and socio-economic factors,emphasizing the importance of these variables in predicting flood susceptibility.These results have important implications for disaster relief management and future research should focus on refining these models to improve predictive accuracy and exploring socio-economic factors.展开更多
Calcium fluoride nanoparticles were synthesized by water/cetyltrimethylammonium bromide (CTAB)/2-octanol microemulsion systems. X-ray powder diffraction analysis showed that the products were a single phase. The resul...Calcium fluoride nanoparticles were synthesized by water/cetyltrimethylammonium bromide (CTAB)/2-octanol microemulsion systems. X-ray powder diffraction analysis showed that the products were a single phase. The result of scanning electron microscopy confirmed that the average sizes of the calcium fluoride particles were below 100 nm in diameter. With decreasing water content and reaction time, the particle sizes decreased.展开更多
文摘This study examines how socio-economic characteristics predict flood risk in London,England,using machine learning algorithms.The socio-economic variables considered included race,employment,crime and poverty measures.A stacked generalization(SG)model combines randomforest(RF),support vector machine(SVM),and XGBoost.Binary classification issues employ RF as the basis model and SVM as the meta-model.In multiclass classification problems,RF and SVM are base models while XGBoost is meta-model.The study utilizes flood risk labels for London areas and census data to train these models.This study found that SVM performs well in binary classifications with an accuracy rate of 0.60 and an area under the curve of 0.62.XGBoost outperforms other multiclass classification methods with 0.62 accuracy.Multiclass algorithms may perform similarly to binary classification jobs due to reduced data complexity when combining classes.The statistical significance of the result underscores their robustness,respectively.The findings reveal a significant correlation between flood risk and socio-economic factors,emphasizing the importance of these variables in predicting flood susceptibility.These results have important implications for disaster relief management and future research should focus on refining these models to improve predictive accuracy and exploring socio-economic factors.
文摘Calcium fluoride nanoparticles were synthesized by water/cetyltrimethylammonium bromide (CTAB)/2-octanol microemulsion systems. X-ray powder diffraction analysis showed that the products were a single phase. The result of scanning electron microscopy confirmed that the average sizes of the calcium fluoride particles were below 100 nm in diameter. With decreasing water content and reaction time, the particle sizes decreased.
