A comparative analysis of training methods for artificial neural network rainfall-runoff models
By S. Srinivasulu, Ashu Jain. Applied Soft Computing, 2006
326 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/06340d8c14ecd2ffa41cf78547bda58facfc4f39
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- O. Sabokpa
- A. R. Khalesian
- A. Zarei‐Hanzaki
- John P. Norton
- Anthony J. Jakeman
- Serena H. Chen
- N. Haghdadi
- Hamid Reza Abedi
In Global and Planetary Change
- O. Sabokpa
- A. R. Khalesian
- A. Zarei‐Hanzaki
- John P. Norton
- Anthony J. Jakeman
- Serena H. Chen
- N. Haghdadi
- Hamid Reza Abedi
In Water Science and Technology
- O. Sabokpa
- A. R. Khalesian
- A. Zarei‐Hanzaki
- John P. Norton
- Anthony J. Jakeman
- Serena H. Chen
- N. Haghdadi
- Hamid Reza Abedi
Citations in the corpus
- Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy (2012, methodology): “Hence, the AARE and RMSE are also calculated through a term by term comparison of the relative error and therefore are unbiased statistics for verifying the predictability of a model/equation [46].”
- Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, background): “• GA and ANN: water-quality management [99], rainfall-runoff modelling [149].”
- Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy (2013, methodology): “On the other hand, the AARE is considered as an unbiased statistics for verifying the reliability of a model since it checks the predictability of a model through a term-by-ter m comparison of the relative errors [33].”