Study on the mining geological hazard susceptibility assessment in alpine areas using machine learning models
The fragile geological environment of mines in alpine regions,influenced by glacier activity and human engineering,experiences frequent geological hazards that significantly hinder efficient mining and safe operations.Assessing geological hazard susceptibility has be-come essential for disaster prevention and mitigation,and scientific early warning in mining ar-eas.This study examined the Nuoer Lake iron mine in Tianshan,employing machine learning models such as the J48 decision tree,logical model tree(LMT),and radial basis function clas-sifier(RBFC)to predict geological hazard susceptibility in alpine mining areas.Initially,a geo-spatial database for the study area was created using geological maps,high-resolution remote sensing images,and on-site investigations.This database included 256 geological hazards and 12 conditioning factors,which were randomly split into training(80％)and validation(20％)datasets.Subsequently,geological hazard conditioning factors were selected through multicol-linearity diagnosis and information gain.Finally,the performance of the models was evaluated and validated using the area under the receiver operating characteristic curve(Auc)and statis-tical indices(e.g.,Accuracy,abbreviated as Acc).The results indicate that slope angle,prox-imity to roads,and proximity to rivers are the most significant parameters influencing geolog-ical hazards.The RBFC model attains the highest prediction accuracy(Auc=0.854,Acc=79.41％),followed by the LMT(Auc=0.849,Acc=77.45％)and J48(Auc=0.819,Acc=75.49％)models.Geological hazard high and very high susceptibility zones are primarily located within 1 000 meters of roads and rivers,and within 2 000 meters of mining projects and glaciers.The geological hazard susceptibility maps generated by all three models accurately re-flect the actual distribution of geological hazards in mining areas.This study provides scientific references for site selection and land-use planning in mining engineering,enhancing geological safety assessment and emergency management capabilities.
alpine areasNuoer Lake iron minegeological hazard susceptibilityradial basis function classifiermachine learning
NETL
NSTL
万方数据
