Machine Learning-Based Predictive Model of Ground Subsidence Risk Using Characteristics of Underground Pipelines in Urban Areas.
Sungyeol LeeJaemo KangJinyoung KimPublished in: IEEE Access (2023)
Keyphrases
- predictive model
- urban areas
- machine learning
- decision trees
- historical data
- aerial images
- remote sensing images
- geographic information systems
- artificial neural networks
- satellite images
- learning algorithm
- pattern recognition
- neural network
- prediction model
- feature selection
- support vector machine
- machine learning methods
- travel time
- data management
- change detection
- classification rules
- database
- feature vectors
- bayesian networks
- data mining
- databases