Hamid Darabi
Global and Planetary Change · 2019–2020
A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Global and Planetary Change — 29 citations into it
- Artificial Intelligence — 15 citations into it
- Management, Monitoring, Policy and Law — 15 citations into it
- Ecological Modeling — 11 citations into it
- Ecology — 11 citations into it
- Statistics and Probability — 9 citations into it
- Nature and Landscape Conservation — 8 citations into it
- Mechanical Engineering — 5 citations into it
Whom Hamid Darabi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- R. P. Anderson
- Wei-Yin Loh
- Wei‐Wen Wu
- Betty Chang
- Li Zhu
- Yu-Shan Shih
- Yu-Ting Lee
- A. Townsend Peterson
- Peter Buhlmann
- Giovanni Battista Crosta
Papers from Hamid Darabi's perspective
Ordered by how strongly the researchers Hamid Darabi trusts endorse each paper. Same corpus, a different order for every researcher.
- Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela
- Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
- Generalized Linear Models
- SPLIT SELECTION METHODS FOR CLASSIFICATION TREES
- Predicting distributions of Mexican birds using ecological niche modelling methods
- Random Forests
- A decision-theoretic generalization of on-line learning and an application to boosting
- Boosting for high-dimensional linear models
- Data Mining Practical Machine Learning Tools and Techniques
- Optimization by Simulated Annealing
Their papers
- The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers (2020) — 120 citations
- Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques (2019) — 369 citations
- Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (2019) — 304 citations