Hail characteristics and hail recognition method based on machine learning in Inner Mongolia
Based on the manual observation of hail records in Inner Mongolia,China,from 1959 to 2021,the spa-tial and temporal characteristics of hail distribution are analyzed,and a hail recognition method is constructed based on machine learning algorithms.The results are as follows:(1)Regarding temporal distribution,the num-ber of hail days and affected stations in Inner Mongolia shows a decreasing trend.In terms of spatial distribution,hail events are predominantly concentrated in the Yinshan Mountains and the Greater Hinggan Mountains,with hail-prone areas extending along these mountain ranges.(2)Hail exhibits distinct seasonal and diurnal characteris-tics.The peak hail months in Inner Mongolia are from May to September,accounting for 91.79%of the annual hail days.The most frequent period for hail occurrences is between 12:00 BST and 19:00 BST.(3)Four machine learning algorithms(random forest,LightGBM,K-proximity,and decision tree)are used to model and evaluate hail events in Inner Mongolia through data preprocessing,predictor selection,model training,and tuning.Verifi-cation results indicate that machine learning methods effectively identify hail events,with the threat score of each model exceeding 0.83 and hit rates surpassing 92%.Among these,the random forest algorithm demonstrates the best recognition performance on the test set.These findings provide useful references for hail forecasting and arti-ficial hail prevention in Inner Mongolia.
the number of hail station daystemporal and spatial characteristicsmachine learninghail identifi-cation
万方数据
维普
