Ashu Jain

Environmental Engineering · 2006–2010

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 24 citations into it; #666 of 5,479 by trust here, #956 by citations
• Artificial Intelligence — 10 citations into it
• Electrical and Electronic Engineering — 8 citations into it; #2,382 of 16,539 by trust here, #5,811 by citations

Whom Ashu Jain trusts overall

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

1. Holger R. Maier
2. Anthony J. Jakeman
3. Aman Mohammad Kalteh
4. Graeme C. Dandy
5. Robert J. May

Who trusts Ashu Jain overall

1. Shun-Yao Shih
2. Guokun Lai
3. L. Diaz-Robles
4. Salim Heddam
5. Mohammad Valipour
6. Mehdi Khashei
7. N. Haghdadi
8. Abdüsselam Altunkaynak
9. Saman Razavi
10. Fan-Keng Sun

Papers from Ashu Jain's perspective

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

1. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications (Environmental Modelling & Software)
2. Ten iterative steps in development and evaluation of environmental models (Environmental Modelling & Software)
3. Neural Networks for Pattern Recognition
4. Modeling of the daily rainfall-runoff relationship with artificial neural network
5. Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application (Environmental Modelling & Software)
6. Non-linear variable selection for artificial neural networks using partial mutual information (Environmental Modelling & Software)
7. Fundamentals of Artificial Neural Networks
8. Neural Networks - A Systematic Introduction
9. River flow prediction using artificial neural networks: generalisation beyond the calibration range.
10. A new look at the statistical model identification

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
• Hybrid neural network models for hydrologic time series forecasting (2007) — 460 citations
• A comparative analysis of training methods for artificial neural network rainfall-runoff models (2006) — 326 citations