Christoph Reudenbach

Environmental Engineering · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 14 citations into it; #2,222 of 5,479 by trust here, #3,944 by citations
• Ecology — 7 citations into it; #8,169 of 10,717 by trust here, #10,333 by citations
• Ecological Modeling — 5 citations into it
• Nature and Landscape Conservation — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Christoph Reudenbach trusts overall

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

1. Max Kuhn
2. Alexander Brenning
3. V. Bahn
4. Trevor J. Hastie
5. David John Lary
6. C. Brungard
7. Roozbeh Valavi
8. Hanna Meyer

Who trusts Christoph Reudenbach overall

1. Hanna Meyer
2. Alexandre M. J.-C. Wadoux
3. Edzer J. Pebesma
4. Budiman Minasny

Papers from Christoph Reudenbach's perspective

Ordered by how strongly the researchers Christoph Reudenbach trusts endorse each paper. Same corpus, a different order for every researcher.

1. Applied Predictive Modeling
2. Testing the predictive performance of distribution models
3. Assessing transferability of ecological models: an underappreciated aspect of statistical validation
4. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation (Environmental Modelling & Software)
5. Spatial prediction models for landslide hazards: review, comparison and evaluation
6. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
7. Application of machine learning methods to spatial interpolation of environmental variables (Environmental Modelling & Software)
8. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (PeerJ)
9. caret: Classification and Regression Training
10. Machine learning for predicting soil classes in three semi-arid landscapes

Their papers

• Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction (2019) — 401 citations
• Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation (2018) — 446 citations