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.
References
- Bélanger, M., El-Jabi, N., Caissie, D., Ashkar, F., Ribi, J.M. (2005). Estimation de la température de l'eau en rivière en utilisant les réseaux de neurones et la régression multiple. Revue des Sciences de l'Eau, 18(3), 403-421.
- Caissie, D. (2004). Stream temperature modeling in forest catchments. PhD Thesis, Dalhousie University, Halifax, NS, 207p.
- Caissie, D. (2006). The thermal regime of rivers: a review. Freshwater Biology, 51, 1389-1406.
- Caissie, D., El-Jabi, N., St-Hilaire, A. (1998). Stochastic modelling of water temperatures in a small stream using air to water relations. Canadian Journal of Civil Engineering, 25, 250-260.
- Caissie, D., Satish, M. G., El-Jabi, N. (2005). Predicting river water temperatures using the equilibrium temperature concept method with applications on Miramichi River Catchments (New Brunswick, Canada). Hydrological Processes, 19, 2137-2159.
- Caissie, D., Satish, M. G., El-Jabi, N. (2007). Predicting water temperatures using a deterministic model: Application on Miramichi River catchments (New Brunswick, Canada). Journal of Hydrology, 336(3-4), 303-315.
- Chenard, J., Caissie, D. (2008). Stream temperature modelling using artificial neural networks: application on Catamaran Brook, New Brunswick, Canada. Hydrological Processes, 22(17), 3361-3372.
- Cluis, D. A. (1972). Relationship between stream water temperature and ambient air temperature - a simple autoregressive model for mean daily stream temperature fluctuations. Nordic Hydrology, 3, 1025- 1031.
- Cunjak, R. A., Caissie, D., El-Jabi, N. (1990). The Catamaran Brook Habitat Research Project: description and general design of study. Canadian Technical Report of Fisheries and Aquatic Sciences, 1751, 14.
- Govindaraju, R. S. (2000). Artificial neural networks in hydrology. I: Preliminary concepts. Journal of hydrologic Engineering, 5(2), 115-123.
- Hebert, C., Caissie, D., Satish, M. G., El-Jabi, N. (2011). Study of stream temperature dynamics and corresponding heat fluxes within Miramichi River catchments (New Brunswick, Canada). Hydrological Processes, 25, 2439-2455.
- Jain, A. K., Mao, J., Mohiuddin, K. M. 1996. Artificial neural network: A tutorial. Computer, 31-44.
- Johnston, T. A. (1997). Downstream movement of youngof-the-year fishes in Catamaran Brook and the Little Southwest Miramichi River, New Brunswick. Journal of Fish Biology, 51, 1047-1062.
- Kothandaraman, V. (1971). Analysis of water temperature variations in large rivers. Journal of the Sanitary Engineering Division, 97, 19-31.
- Mohseni, O., Stefan, H. G. (1999). Stream temperature-air temperature relationships: a physical interpretation. Journal of Hydrology, 218, 128-141.
- Risley, J. C., Roehl, E. A. Jr., Conrads, P. A. (2003). Estimating water temperatures in small stream in Western Oregon using neural network models. U.S. Geological Survey (USGS) Water-Resources Investigations Report 02-4218, 59p.
- Sivri, N., Kilic, N., Ucan, O. (2007). Estimation of stream temperature in Firtinia Creek (Rize-Turkiye) using artificial neural network model. Journal of Environmental Biology, 28(1), 67-72.
- Smith, M. 1993. Neural networks for statistical analysis. Van Nostrand Reinhold, New York, 235p.
- Song, C. C. S., Chen, C. Y. (1977). Stochastic properties of daily temperature in rivers. Journal of the Environmental Engineering Division, 103, 217-231.
- Stefan, H. G., Preud'homme, E. B. 1993. Stream temperature estimation from air temperature. Water Resources Bulletin, 29, 27-45.
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