Risk Assessment of Groundwater Depletion Induced Land Subsidence: A Case Study in Taiyuan Basin, China

Groundwater depletion induced land subsidence affects the safety of local communities. In this research, a data-driven approach is applied to predict and assess the risk level of land subsidence in Taiyuan Basin, Shanxi Province, China based on Geographic Information Systems data and field investiga...

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Bibliographic Details
Published in:Geotechnical and geological engineering 2020, Vol.38 (1), p.985-994
Main Authors: Liu, Yong, Liao, Yan
Format: Article
Language:English
Subjects:
Algorithms
Benchmarks
Civil Engineering
Classification
Computer applications
Decision trees
Depletion
Earth and Environmental Science
Earth Sciences
Field investigations
Geographic information systems
Geographical information systems
Geotechnical Engineering & Applied Earth Sciences
Groundwater
Groundwater depletion
Hydrogeology
Information systems
Land subsidence
Learning algorithms
Local communities
Machine learning
Multilayers
Remote sensing
Risk assessment
Risk levels
Sensitivity analysis
Specificity
Subsidence
Support vector machines
Technical Note
Terrestrial Pollution
Waste Management/Waste Technology
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Summary:Groundwater depletion induced land subsidence affects the safety of local communities. In this research, a data-driven approach is applied to predict and assess the risk level of land subsidence in Taiyuan Basin, Shanxi Province, China based on Geographic Information Systems data and field investigation data. First, the relevance vector machine is introduced to model and classify the risky/non-risky ground subsidence cases. Next, an ensemble classifier using multiple relevance vector machines and elastic net is proposed in this research. Multiple land subsidence locations in Taiyuan Basin have been investigated in this study. Five benchmarking machine learning classification algorithms including decision tree, random forest, multi-layer perceptron, support vector machine and classical relevance vector machine have been compared in this study. Four evaluation metrics including accuracy, sensitivity, specificity and area under the receiver operating characteristic curve have been introduced to assess the classification performance of all the algorithms tested. Computational results demonstrated the outperformance of the proposed approach in classifying risky land subsidence cases compared with other benchmarking algorithms.
ISSN:0960-3182
1573-1529
DOI:10.1007/s10706-019-01060-3