Indra Prakash

Management, Monitoring, Policy and Law · 2016–2021

A researcher in the Science corpus: 9 papers; trusts 41 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

Whom Indra Prakash trusts overall

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

  1. Biswajeet Pradhan
  2. D. Tien Bui
  3. Saro Lee
  4. Vladimir Naumovich Vapni
  5. Hamid Reza Pourghasemi
  6. Binh Thai Pham
  7. Mark A. Hall
  8. Niels Landwehr
  9. Trevor J. Hastie
  10. Filippo Catani

Who trusts Indra Prakash overall

  1. Khabat Khosravi
  2. Wei Chen
  3. Binh Thai Pham
  4. D. Tien Bui
  5. Himan Shahabi

Papers from Indra Prakash's perspective

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

  1. 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)
  2. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression (Landslides)
  3. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps
  4. The Nature of Statistical Learning Theory
  5. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) (Computational Geosciences)
  6. Random Forests
  7. Statistical analysis of landslide susceptibility at Yongin, Korea
  8. Application of an evidential belief function model in landslide susceptibility mapping (Computational Geosciences)
  9. Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey
  10. Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey)

Their papers