Authors:
Cindie Hébert
1
;
Daniel Caissie
2
;
Mysore G. Satish
1
and
Nassir El-Jabi
3
Affiliations:
1
Dalhousie University, Canada
;
2
Fisheries and Oceans, Canada
;
3
Université de Moncton, Canada
Keyword(s):
River/Streams, Modeling, Temperature, Artificial Neural Network.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
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.