Spatial prediction of ground subsidence susceptibility using an artificial neural network

Environmental Management

Volumn 49, Issue 2, 2012, Pages 347-358

  Lee, Saro ^a   Park, Inhye ^b   Choi, Jong Kuk ^c  

^a Korea Institute of Geosceince and Mineral Resources   (South Korea)

^b University of Seoul   (South Korea)

^c Korea Research Institute of Ships and Ocean Engineering   (South Korea)

Author keywords

Abandoned underground coal mine; Artificial neural networks; GIS; Ground subsidence; Korea

Indexed keywords

ANALYSIS MODELS; ARTIFICIAL NEURAL NETWORK; INFRASTRUCTURE PLANNING; INPUT FACTORS; KOREA; LOSS OF LIFE; MAJOR FACTORS; PREDICTION ACCURACY; RELATIVE IMPORTANCE; ROCK-MASS; SOUTH KOREA; SPATIAL DATA; SPATIAL PREDICTION; TEST SETS; TRAINING ALGORITHMS; TRAINING DATA SETS; TRAINING SETS; TRANSPORTATION NETWORK; UNDERGROUND COAL MINE;

BACKPROPAGATION ALGORITHMS; COAL MINES; GEOGRAPHIC INFORMATION SYSTEMS; GEOLOGY; LAND USE; SUBSIDENCE;

NEURAL NETWORKS;

GROUND WATER;

ABANDONED MINE; ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; COAL MINE; GEOLOGICAL MAPPING; GIS; INFRASTRUCTURE PLANNING; LAND USE PLANNING; MODEL VALIDATION; PREDICTION; SPATIAL DATA; SPATIAL PLANNING; SUBSIDENCE; UNDERGROUND CONSTRUCTION;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; COAL MINING; CONTROLLED STUDY; ENVIRONMENTAL IMPACT ASSESSMENT; ENVIRONMENTAL PARAMETERS; GEOGRAPHIC INFORMATION SYSTEM; GEOLOGY; GROUND SUBSIDENCE SUSCEPTIBILITY; HAZARD ASSESSMENT; LAND USE; LEARNING ALGORITHM; PREDICTION; PROCESS MODEL; QUANTITATIVE ANALYSIS; SOUTH KOREA; TOPOGRAPHY; VALIDATION PROCESS;

COAL MINING; GEOGRAPHIC INFORMATION SYSTEMS; GEOLOGICAL PROCESSES; NEURAL NETWORKS (COMPUTER); REPUBLIC OF KOREA;

KANGWON; SOUTH KOREA;

EID: 84856977498     PISSN: 0364152X     EISSN: 14321009     Source Type: Journal    
DOI: 10.1007/s00267-011-9766-5     Document Type: Article

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