首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Sinkhole hazard assessment in Minnesota using a decision tree model
Authors:Yongli Gao  Jr" target="_blank">E Calvin AlexanderJr
Institution:(1) Department of Physics, Astronomy and Geology, East Tennessee State University, Johnson City, TN 37614, USA;(2) Department of Geology and Geophysics, University of Minnesota, 310 Pillsbury Dr., SE, Minneapolis, MN 55455, USA
Abstract:An understanding of what influences sinkhole formation and the ability to accurately predict sinkhole hazards is critical to environmental management efforts in the karst lands of southeastern Minnesota. Based on the distribution of distances to the nearest sinkhole, sinkhole density, bedrock geology and depth to bedrock in southeastern Minnesota and northwestern Iowa, a decision tree model has been developed to construct maps of sinkhole probability in Minnesota. The decision tree model was converted as cartographic models and implemented in ArcGIS to create a preliminary sinkhole probability map in Goodhue, Wabasha, Olmsted, Fillmore, and Mower Counties. This model quantifies bedrock geology, depth to bedrock, sinkhole density, and neighborhood effects in southeastern Minnesota but excludes potential controlling factors such as structural control, topographic settings, human activities and land-use. The sinkhole probability map needs to be verified and updated as more sinkholes are mapped and more information about sinkhole formation is obtained.
Keywords:Decision tree model  Sinkhole probability  Karst feature database (KFD)  Knowledge discovery in database (KDD)  Nearest neighbor analysis (NNA)  Minnesota
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号