Seyed Amir Naghibi

Global and Planetary Change · 2015–2018

A researcher in the Science corpus: 6 papers; trusts 23 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Global and Planetary Change — 32 citations into it; #3,236 of 10,225 by trust here, #4,099 by citations
• Management, Monitoring, Policy and Law — 32 citations into it
• Artificial Intelligence — 20 citations into it
• Mechanical Engineering — 14 citations into it
• Safety, Risk, Reliability and Quality — 12 citations into it
• Ecological Modeling — 9 citations into it
• Environmental Engineering — 9 citations into it; #1,945 of 5,479 by trust here, #1,854 by citations
• Nature and Landscape Conservation — 9 citations into it

Whom Seyed Amir Naghibi trusts overall

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

1. Biswajeet Pradhan
2. Leo Breiman
3. Hamid Reza Pourghasemi
4. Robert E. Schapire
5. Isik Yilmaz
6. Saro Lee
7. David Ruppert
8. Tin Kam Ho
9. Trevor J. Hastie
10. André Stumpf

Who trusts Seyed Amir Naghibi overall

1. Hamid Reza Pourghasemi
2. Haoyuan Hong
3. Binh Thai Pham
4. Khabat Khosravi
5. Wei Chen
6. Bahareh Kalantar
7. Shaghayegh Miraki
8. Biswajeet Pradhan
9. Mostafa Moradi Dashtpagerdi
10. Amiya Gayen

Papers from Seyed Amir Naghibi's perspective

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

1. Bagging Predictors
2. Random Forests
3. 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)
4. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran (Proceedings of the Indian Academy of Sciences, Earth and Planetary Sciences)
5. Predicting species distribution: offering more than simple habitat models. (Ecology Letters)
6. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela
7. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
8. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) (Computational Geosciences)
9. Support-Vector Networks
10. An introduction to ROC analysis

Their papers

• Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018) — 479 citations
• Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping (2017) — 430 citations
• A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping (2017) — 204 citations
• A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China (2017) — 117 citations
• GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015) — 653 citations
• Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran (2015) — 319 citations