K. Sudheer

Environmental Engineering · 2002–2013

A researcher in the Science corpus: 6 papers; trusts 19 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 37 citations into it; #367 of 5,479 by trust here, #442 by citations
• Water Science and Technology — 31 citations into it; #96 of 1,711 by trust here, #377 by citations
• Artificial Intelligence — 18 citations into it; #20,168 of 35,345 by trust here, #15,866 by citations
• Global and Planetary Change — 15 citations into it; #1,907 of 10,225 by trust here, #4,701 by citations
• Electrical and Electronic Engineering — 9 citations into it; #2,787 of 16,539 by trust here, #5,046 by citations
• Ecology — 6 citations into it
• Statistics and Probability — 5 citations into it

Whom K. Sudheer trusts overall

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

1. N. Null
2. David John Cameron MacKay
3. David E. Goldberg
4. M. Stone
5. Holger R. Maier
6. S. Sharma
7. Durga L. Shrestha
8. Gavin J. Bowden
9. George Chryssolouris
10. Graeme C. Dandy

Who trusts K. Sudheer overall

1. C. Wu
2. Kwok-Wing Chau
3. Ozgur Kisi
4. K. Kasiviswanathan
5. F. Mekanik
6. Wenchao Wang
7. Qi Feng
8. Jianyi Lin
9. Chun-tian Cheng
10. Purna C. Nayak

Papers from K. Sudheer's perspective

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

1. Neural Networks for Pattern Recognition
2. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications (Environmental Modelling & Software)
3. Non-linear variable selection for artificial neural networks using partial mutual information (Environmental Modelling & Software)
4. Multilayer feedforward networks are universal approximators
5. Ten iterative steps in development and evaluation of environmental models (Environmental Modelling & Software)
6. Modeling of the daily rainfall-runoff relationship with artificial neural network
7. Detecting change in vague interpretations of landscapes
8. Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application (Environmental Modelling & Software)
9. Cross‐Validatory Choice and Assessment of Statistical Predictions
10. Fundamentals of Artificial Neural Networks

Their papers

• Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions (2010) — 857 citations
• A neuro-fuzzy computing technique for modeling hydrological time series (2004) — 676 citations
• Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations (2013) — 94 citations
• Quantification of the predictive uncertainty of artificial neural network based river flow forecast models (2012) — 86 citations
• Fitting of Hydrologic Models: A Close Look at the Nash–Sutcliffe Index (2008) — 217 citations
• A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models (2002) — 433 citations