Chang-Wook Lee

Management, Monitoring, Policy and Law · 2012–2018

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Management, Monitoring, Policy and Law — 20 citations into it
• Global and Planetary Change — 16 citations into it
• Mechanical Engineering — 14 citations into it

Whom Chang-Wook Lee trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Biswajeet Pradhan
2. Saro Lee

Papers from Chang-Wook Lee's perspective

Ordered by how strongly the researchers Chang-Wook Lee trusts endorse each paper. Same corpus, a different order for every researcher.

1. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling (Environmental Modelling & Software)
2. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela
3. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression (Landslides)
4. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. (Science of the Total Environment)
5. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances (Natural Hazards)
6. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS (Computational Geosciences)
7. How can statistical models help to determine driving factors of landslides
8. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (Environmental Monitoring & Assessment)
9. Top 10 algorithms in data mining
10. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size

Their papers

• Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018) — 116 citations
• Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS (2012) — 303 citations