@inproceedings{a9e6acf1b296494796a04116b1e2adee,
title = "Application of artificial neural networks for river regime",
abstract = "Predicting the geometric characteristics of a regime channel is of utmost importance in the context of river engineering and management, as regime channels require minimum protection and minimum expenses for their maintenance. There are numerous empirical and analytical methods to predict these geometric characteristics. This paper develops and tests an Artificial Neural Network (ANN) as a model to forecast the river regime characteristics. ANN performance is compared against the Thermodynamic Entropy Theory of Yalin and da Silva (2001) and the Stability Theory of Julien and Wargadalam (1995). An improvement in the results of the ANN model has been achieved by distinguishing the input variables into sand and gravel bed materials as well as different discharge groups.",
author = "Bui, {M. D.} and D. Huber and K. Kaveh and {da Silva}, {A. M.F.} and P. Rutschmann",
note = "Publisher Copyright: {\textcopyright} 2016 Taylor & Francis Group, London.; International Conference on Fluvial Hydraulics, RIVER FLOW 2016 ; Conference date: 11-07-2016 Through 14-07-2016",
year = "2016",
doi = "10.1201/9781315644479-28",
language = "English",
isbn = "9781138029132",
series = "River Flow - Proceedings of the International Conference on Fluvial Hydraulics, RIVER FLOW 2016",
publisher = "CRC Press/Balkema",
pages = "154--160",
editor = "George Constantinescu and Marcelo Garcia and Dan Hanes",
booktitle = "River Flow - Proceedings of the International Conference on Fluvial Hydraulics, RIVER FLOW 2016",
}