Graeme C. Dandy

Environmental Engineering · 2000–2014

A researcher in the Science corpus: 8 papers; trusts 47 researchers and is trusted by 78 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 55 citations into it; #60 of 5,479 by trust here, #168 by citations
• Water Science and Technology — 46 citations into it; #49 of 1,711 by trust here, #395 by citations
• Artificial Intelligence — 43 citations into it; #2,995 of 35,345 by trust here, #4,526 by citations
• Computational Theory and Mathematics — 24 citations into it
• Global and Planetary Change — 23 citations into it
• Statistics and Probability — 20 citations into it
• Ocean Engineering — 15 citations into it
• Model Architectures — 13 citations into it

Whom Graeme C. Dandy trusts overall

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

1. Ashish Sharma
2. Holger R. Maier
3. Teuvo Kohonen
4. Roberto Battiti
5. David E. Goldberg
6. Scott E. Fahlman
7. Xin Yao
8. Gavin J. Bowden
9. Robert A. Jacobs
10. Jeffrey L. Elman

Who trusts Graeme C. Dandy overall

1. Holger R. Maier
2. Wenyan Wu
3. Kwok-Wing Chau
4. John M. Quilty
5. Salim Heddam
6. Aman Mohammad Kalteh
7. Riccardo Taormina
8. C. Wu
9. Jan Adamowski
10. Saman Razavi

Papers from Graeme C. Dandy's perspective

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

1. Long Short-Term Memory
2. Learning internal representations by error propagation
3. ImageNet classification with deep convolutional neural networks
4. Neural Networks for Pattern Recognition
5. A new look at the statistical model identification
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Information Theory: 1948-1998 - Guest Editorial
8. Finding Structure in Time
9. Gradient-based learning applied to document recognition
10. Genetic Algorithms in Search

Their papers

• Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications (2000) — 2,376 citations
• 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
• Optimal division of data for neural network models in water resources applications (2002) — 324 citations
• Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014) — 317 citations
• Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014) — 596 citations
• Review of Input Variable Selection Methods for Artificial Neural Networks (2011) — 389 citations
• Non-linear variable selection for artificial neural networks using partial mutual information (2008) — 312 citations
• Input determination for neural network models in water resources applications. Part 1—background and methodology (2005) — 550 citations