Modeling of River Water Temperatures using Feed-forward Artificial Neural Networks

Cindie Hébert, Daniel Caissie, Mysore G. Satish, Nassir El-Jabi

2012

Abstract

Water temperature influences most physical, chemical and biological processes of the river environment. It plays an important role in the distribution of fishes and on the growth rates of many aquatic organisms. It is therefore important to develop water temperature models in order to effectively manage aquatic habitats, to study the thermal regime of rivers and to have effective tools for environmental impact studies. The objective of the present study was to develop a water temperature model based on artificial neural networks (ANN) for two thermally different watercourses. The ANN model performed best in summer and autumn and showed a poorer (but still good) performance in spring. The many advantages of ANN models are their simplicity, low data requirements, their capability of modelling long-term series as well as have an overall good performance.

Paper Citation

in Harvard Style

Hébert C., Caissie D., G. Satish M. and El-Jabi N. (2012). Modeling of River Water Temperatures using Feed-forward Artificial Neural Networks . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 558-562. DOI: 10.5220/0004158005580562

in Bibtex Style

@conference{ncta12,
author={Cindie Hébert and Daniel Caissie and Mysore G. Satish and Nassir El-Jabi},
title={Modeling of River Water Temperatures using Feed-forward Artificial Neural Networks},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={558-562},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004158005580562},
isbn={978-989-8565-33-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Modeling of River Water Temperatures using Feed-forward Artificial Neural Networks
SN - 978-989-8565-33-4
AU - Hébert C.
AU - Caissie D.
AU - G. Satish M.
AU - El-Jabi N.
PY - 2012
SP - 558
EP - 562
DO - 10.5220/0004158005580562