Ensemble models for identifying automatically aeolian saltating tracks driven by datasets
It is very vital for tracking sand particle to establish automatic identification of saltating tracks.Thus,the four ensemble models,including the Extremely randomized trees,the Random forests,the XGBoost,and the Gradient Boosting Decision Tree driven by the datasets we constructed,were proposed for identifying saltat-ing tracks.Firstly,all the models perform well in spite of the dataset without very good discriminability,suggest-ing these models own an advantage when dealing with nonlinear relationships.Secondly,the Extremely random-ized trees model holds the highest accuracy(0.9035),precision(0.9030),recall(0.9035),F1 score(0.8995),MCC(0.7378),and AUC score(0.9179),and time cost while the XGBoost model has the best balance between the higher scores and lower time cost.It implies that the former is most feasible for identifying offline saltating tracks and that the latter is prospective for tracking sand particle online.Finally,the improved datasets,which in-corporate standard deviation of instant horizontal and vertical velocities,significantly enhance the predictive per-formances of Extremely randomized trees.This study effectively reduces the time cost of manual trajectory verifi-cation and broadens the application of machine learning in saltation.
aeolian saltatingextremely randomized treesXGBoostrandom forestgradient boosting deci-sion tree
NETL
NSTL
万方数据
