The municipality of Hammam N’bails,located 37 km east of the capital of Guelma province(eastern Algeria),is accessible via RN20 and CW19 roads.It borders the municipalities of Khemissa and El Henancha in Souk-Ahras p...The municipality of Hammam N’bails,located 37 km east of the capital of Guelma province(eastern Algeria),is accessible via RN20 and CW19 roads.It borders the municipalities of Khemissa and El Henancha in Souk-Ahras province.With a population of approximately 16,000 and covering an area of 164 km²,this region is characterized by mountainous terrain,with elevations ranging from 112 to 292 meters.The area experiences cold,snowy winters and hot,dry summers,with an average annual rainfall of about 600 mm.Renowned for its natural thermal springs,Hammam N’bails is also a notable tourist destination.The rugged topography of the region leads to frequent landslides,particularly on medium and low slopes.Landslide susceptibility is assessed using raster calculators in ArcGIS and efficient machine learning algorithms,such as Decision Trees,Bagging,Random Forest,SVM,and MLP.Factors considered in the analysis include slope,elevation,geology,aspect,proximity to streams and roads,land cover,and rainfall.The performance of these models is evaluated using ROC-AUC curves,providing a robust method to understand and mitigate geological risks in this area.展开更多
文摘The municipality of Hammam N’bails,located 37 km east of the capital of Guelma province(eastern Algeria),is accessible via RN20 and CW19 roads.It borders the municipalities of Khemissa and El Henancha in Souk-Ahras province.With a population of approximately 16,000 and covering an area of 164 km²,this region is characterized by mountainous terrain,with elevations ranging from 112 to 292 meters.The area experiences cold,snowy winters and hot,dry summers,with an average annual rainfall of about 600 mm.Renowned for its natural thermal springs,Hammam N’bails is also a notable tourist destination.The rugged topography of the region leads to frequent landslides,particularly on medium and low slopes.Landslide susceptibility is assessed using raster calculators in ArcGIS and efficient machine learning algorithms,such as Decision Trees,Bagging,Random Forest,SVM,and MLP.Factors considered in the analysis include slope,elevation,geology,aspect,proximity to streams and roads,land cover,and rainfall.The performance of these models is evaluated using ROC-AUC curves,providing a robust method to understand and mitigate geological risks in this area.
