Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
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Title
Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
Authors
Keywords
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Journal
Landslides
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-12-23
DOI
10.1007/s10346-022-01998-1
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Note: Only part of the references are listed.- Application of a two-step sampling strategy based on deep neural network for landslide susceptibility mapping
- (2022) Jingyu Yao et al. Bulletin of Engineering Geology and the Environment
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- (2022) Sharad Kumar Gupta et al. Environmental Earth Sciences
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- (2021) Husam A. H. Al-Najjar et al. Remote Sensing
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- (2021) Daisuke Matsuoka Progress in Earth and Planetary Science
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- (2020) Zhengjing Ma et al. NEURAL COMPUTING & APPLICATIONS
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- (2019) Yumiao Wang et al. International Journal of Environmental Research and Public Health
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- (2019) Xiangang Luo et al. PLoS One
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- (2019) Diego Cantorna et al. APPLIED SOFT COMPUTING
- Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping
- (2019) Hamid Reza Pourghasemi et al. CATENA
- Landslide mapping with remote sensing: challenges and opportunities
- (2019) Cheng Zhong et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images
- (2019) Lingcao Huang et al. REMOTE SENSING OF ENVIRONMENT
- Selection of weightages for causative factors used in preparation of landslide susceptibility zonation (LSZ)
- (2018) Sharad Kumar Gupta et al. Geomatics Natural Hazards & Risk
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- (2018) Faith E. Taylor et al. EARTH SURFACE PROCESSES AND LANDFORMS
- Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping
- (2018) Prima Kadavi et al. Remote Sensing
- The area under the precision-recall curve as a performance metric for rare binary events
- (2018) H.R. Sofaer et al. Methods in Ecology and Evolution
- Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China)
- (2018) Yingxu Song et al. ISPRS International Journal of Geo-Information
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- (2017) Mariantonietta Ciurleo et al. ENGINEERING GEOLOGY
- Learning from class-imbalanced data: Review of methods and applications
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- Identification of landslide-prone zones in the geomorphically and climatically sensitive Mandakini valley, (central Himalaya), for disaster governance using the Weights of Evidence method
- (2017) Poonam et al. GEOMORPHOLOGY
- Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India
- (2017) Deepak Kumar et al. GEOMORPHOLOGY
- Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias
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- Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines
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- Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree
- (2015) Dieu Tien Bui et al. Landslides
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- (2015) Loris Nanni et al. NEUROCOMPUTING
- Landslides triggered by the June 2013 extreme rainfall event in parts of Uttarakhand state, India
- (2014) Tapas R. Martha et al. Landslides
- A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
- (2014) Omar F. Althuwaynee et al. Landslides
- Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
- (2013) Natan Micheletti et al. Mathematical Geosciences
- GIS-based morpho-tectonic studies of Alaknanda river basin: a precursor for hazard zonation
- (2013) D. P. Shukla et al. NATURAL HAZARDS
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- Landslide inventory maps: New tools for an old problem
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- (2010) Yogesh Ray et al. QUATERNARY SCIENCE REVIEWS
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- (2009) Julien Célérier et al. GEOLOGICAL SOCIETY OF AMERICA BULLETIN
- Learning from Imbalanced Data
- (2009) Haibo He et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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- (2008) Yuchun Tang et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
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- (2008) Ajay Mathur et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
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