Hanna Meyer

Environmental Engineering · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 20 citations into it; #2,707 of 5,479 by trust here, #3,797 by citations
• Ecology — 13 citations into it; #10,027 of 10,717 by trust here, #10,333 by citations
• Ecological Modeling — 12 citations into it
• Nature and Landscape Conservation — 11 citations into it
• Artificial Intelligence — 10 citations into it
• Statistics and Probability — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Economics and Econometrics — 6 citations into it

Whom Hanna Meyer trusts overall

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

1. Max Kuhn
2. Trevor J. Hastie
3. Alexander Brenning
4. Roozbeh Valavi
5. Andy Liaw
6. V. Bahn
7. Jane Elith
8. Tomislav Hengl
9. Christoph Reudenbach
10. Leo Breiman

Who trusts Hanna Meyer overall

1. Alexandre M. J.-C. Wadoux
2. Edzer J. Pebesma
3. Budiman Minasny
4. Patrick Schratz
5. Aleksandar Sekulić
6. Alex B. McBratney
7. Maximilian Pichler
8. Christoph Reudenbach

Papers from Hanna Meyer's perspective

Ordered by how strongly the researchers Hanna Meyer 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. Random Forests
4. Assessing transferability of ecological models: an underappreciated aspect of statistical validation
5. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
6. Statistics For Spatial Data
7. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation (Environmental Modelling & Software)
8. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
9. Spatial prediction models for landslide hazards: review, comparison and evaluation
10. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

Their papers

• Predicting into unknown space? Estimating the area of applicability of spatial prediction models (2020) — 416 citations
• 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