An empirical study on sea water quality prediction

Authors:
Evaggelos V. Hatzikos;Grigorios Tsoumakas;George Tzanis;Nick Bassiliades;Ioannis Vlahavas
Affiliations:
Department of Automation, Technological Educational Institute of Thessaloniki, P.O. Box 141, 57400 Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Venue:
Knowledge-Based Systems
Year:
2008

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Abstract

This paper studies the problem of predicting future values for a number of water quality variables, based on measurements from under-water sensors. It performs both exploratory and automatic analysis of the collected data with a variety of linear and nonlinear modeling methods. The paper investigates issues, such as the ability to predict future values for a varying number of days ahead and the effect of including values from a varying number of past days. Experimental results provide interesting insights on the predictability of the target variables and the performance of the different learning algorithms.