| Title: |
An Intelligent System for Monitoring and Predicting Water Quality |
| Author(s): |
N. Bassiliades, I. Antoniades, E. Hatzikos, I. Vlahavas, G. Koutitas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (9 pages).
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| Keywords: |
Sensor Network, Pollution Monitoring, Aquatic Uses, Water quality, Pollution Prediction
Pollution Pre
diction
Prediction, Knowledge-Based System.
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| Appeared in: |
Proceedings of the European conference TOWARDS eENVIRONMENT, pp. 534-542, Prague, Czech Republic, March 2009, 2009.
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| Abstract: |
In this paper we present an intelligent system for monitoring and predicting water quality, whose main aim is to help the authorities in the "decision-making" process in the battle against the pollution of the aquatic environment, which is very vital for the public health and the economy of Northern Greece. Two sensor-telematic networks for collecting water quality measurements in real time (Andromeda, for sea waters, and Interrisk, for surface/fresh waters) were developed and deployed. Sensor readings (water temperature, pH, dissolved oxygen, conductance, turbidity, sea currents, and salinity) are transmitted to a main station for processing and storage. The intelligent system monitors sensor data, reasons, using fuzzy logic, about the current level of water suitability for various aquatic uses, such as swimming and piscicultures, and flags out appropriate alerts. Furthermore, the system employs Machine Learning and Adaptive Filtering techniques and algorithms which successfully predict measurements a day ahead, as well as techniques to incorporate the window of past values in order to be able to make a more precise prediction. The results showed that these algorithms can help make accurate predictions one day ahead and are better than the naive prediction that the value will be similar to today. |
| See also : |
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This paper has been cited by the following:
| 1 |
Mahmoud, S., El-Bendary, N., Mahmood, M.A., Hassanien, A.E., “An intelligent recommender system for drinking water quality”, (2014) 13th International Conference on Hybrid Intelligent Systems, HIS 2013, art. no. 6920498, pp. 285-290. |
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