Taking the Bailong River Basin,where debris flow disasters are extremely developed,as the study area,the temporal and spatial laws of regional debris flow outbreaks were analyzed,and the proba-bility prediction model of debris flow damming constructed to realize the risk prediction of debris flow damming disasters within the region,hoping to provide a reference for the risk prevention and control of regional debris flow chain disasters.Based on the temporal and spatial pattern of large-scale debris flows in the Bailong River Basin,and on the basis of field investigation and data collection,we selected 10 parameters,including drainage area,drainage height difference,gully bed gradient,lithology,landslide density,annual average times of daily rainfall greater than 50 mm,river width,river discharge and river gradient,as the key factors determining whether debris flow gullies would block the river.Based on the machine learning model,a prediction model for the probability of debris flow blocking the river was con-structed,whose accuracy was 91.3％,and the area under the receiver operating characteristic curve was 0.931.Research has concluded that river width is the most important influencing factor for debris flow blockage disasters,followed by drainage area,gully bed gradient,drainage height difference and land-slide density.
debris flowriver blockinghazardinfluence factormachine learningBailong River
维普
万方数据
