Marten Geertsema
Global and Planetary Change · 2019–2020
A researcher in the Science corpus: 3 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Global and Planetary Change — 63 citations into it
- Management, Monitoring, Policy and Law — 48 citations into it
- Mechanical Engineering — 18 citations into it
Whom Marten Geertsema trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Papers from Marten Geertsema's perspective
Ordered by how strongly the researchers Marten Geertsema trusts endorse each paper. Same corpus, a different order for every researcher.
- Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. (Science of the Total Environment)
- The Nature of Statistical Learning Theory
- 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… (Landslides)
- The meaning and use of the area under a receiver operating characteristic (ROC) curve.
- Estimating Attributes: Analysis and Extensions of RELIEF
- A decision-theoretic generalization of on-line learning and an application to boosting
- Digital terrain modelling: A review of hydrological, geomorphological, and biological applications
- Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)
- Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment (Scientific Reports)
- GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method
Their papers
- Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran (2020) — 108 citations
- Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020) — 334 citations
- New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019) — 114 citations