Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models
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Title
Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models
Authors
Keywords
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Journal
Applied Sciences-Basel
Volume 9, Issue 6, Pages 1248
Publisher
MDPI AG
Online
2019-03-27
DOI
10.3390/app9061248
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- A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)
- (2016) Arzu Erener et al. ENGINEERING GEOLOGY
- A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)
- (2016) Binh Thai Pham et al. ENVIRONMENTAL MODELLING & SOFTWARE
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- (2015) Adnan Ozdemir Bulletin of Engineering Geology and the Environment
- Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods
- (2015) Binh Thai Pham et al. THEORETICAL AND APPLIED CLIMATOLOGY
- Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea
- (2014) Inhye Park et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
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- Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy)
- (2013) Massimo Conforti et al. CATENA
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- A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)
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- A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey
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- Sensitivity analysis for the GIS-based mapping of the ground subsidence hazard near abandoned underground coal mines
- (2010) Hyun-Joo Oh et al. Environmental Earth Sciences
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- (2009) Jong-Kuk Choi et al. Environmental Earth Sciences
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