Tarun Kumar Raghuvanshi

Management, Monitoring, Policy and Law · 2020

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Tarun Kumar Raghuvanshi trusts overall

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

  1. Saro Lee
  2. Alexander Brenning
  3. Richard P. Lippmann
  4. Biswajeet Pradhan
  5. Hakan Ahmet Nefeslioglu
  6. Mehmet Lütfi Süzen
  7. B. D. Ripley
  8. Taskin Kavzoglu
  9. Thomas L. Saaty
  10. D. Tien Bui

Papers from Tarun Kumar Raghuvanshi's perspective

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

  1. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps
  2. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela
  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. Statistical analysis of landslide susceptibility at Yongin, Korea
  5. Spatial prediction models for landslide hazards: review, comparison and evaluation
  6. Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey)
  7. Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey
  8. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data
  9. Proposal of a method to define areas of landslide hazard and application to an area of the Dolomites, Italy
  10. An introduction to computing with neural nets

Their papers