Next Article in Journal
Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
Next Article in Special Issue
Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
Previous Article in Journal
Fermentation Characteristics of Lactobacillus Plantarum and Pediococcus Species Isolated from Sweet Sorghum Silage and Their Application as Silage Inoculants
Previous Article in Special Issue
Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
Article Menu
Issue 6 (March-2) cover image

Export Article

Open AccessArticle

Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

1
Geo-Environmental Hazard Research Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Deajeon 34132, Korea
2
Division of Science Education, Kangwon National University, Chuncheon Campus, 1 Gangwondaehakgil, Chuncheon-si, Gangwon-do 24341, Korea
3
Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea
4
Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(6), 1248; https://doi.org/10.3390/app9061248
Received: 25 January 2019 / Revised: 21 March 2019 / Accepted: 22 March 2019 / Published: 25 March 2019
  |  
PDF [10406 KB, uploaded 28 March 2019]
  |  

Abstract

To effectively prevent land subsidence over abandoned coal mines, it is necessary to quantitatively identify vulnerable areas. In this study, we evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility (LSS) maps. All models were trained using half of a land subsidence inventory, and validated using the other half of the dataset. The model performance was evaluated by comparing the area under the receiver operating characteristic (ROC) curve of the resulting LSS map for each model. Among all models tested, the logit boost, which is a meta-ensemble machine leaning model, generated LSS maps with the highest accuracy (91.44%), i.e., higher than that of the other Bayesian and functional machine learning models, including the Bayes net (86.42%), naïve Bayes (85.39%), logistic (88.92%), and multilayer perceptron models (86.76%). The LSS maps produced in this study can be used to mitigate subsidence risk for people and important facilities within the study area, and as a foundation for further studies in other regions. View Full-Text
Keywords: land subsidence; Bayes net; naïve Bayes; logistic; multilayer perceptron; logit boost land subsidence; Bayes net; naïve Bayes; logistic; multilayer perceptron; logit boost
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Oh, H.-J.; Syifa, M.; Lee, C.-W.; Lee, S. Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models. Appl. Sci. 2019, 9, 1248.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top