LPIS Home Page
Google Search

Title: An Empirical Study of Sea Water Quality Prediction
Author(s): E. Hatzikos, G. Tsoumakas, G. Tzanis, N. Bassiliades, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file.
Appeared in: Knowledge-Based Systems, Elsevier, 21(6), pp. 471-478, 2008.
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.
See also :

        This paper has been cited by the following:

1 Kotis, K., Papasalouros, A., Nikitakos, N. "Supporting Decision Making in Maritime Environmental Protection with a Knowledge-based Education and Awareness Approach", Proc. AIAI'09 Workshop on Artificial Intelligence Applications in Environmental Protection.
2 Mohammed E. El-Mahrouk, Mohammed F. El-Nady, Mahmoud A. Hegazi, Effect Of Diluted Seawater Irrigation And Exogenous Proline Treatments On Growth, Chemical Composition And Anatomical Characteristics Of Conocarpus Erectus L., J. Agric. Res. Kafer El-Sheikh Univ., 36(4), 2010, pp. 420-446.
3 Wang Xuan; Lv Jiake; Xie Deti; , "A hybrid approach of support vector machine with particle swarm optimization for water quality prediction," Computer Science and Education (ICCSE), 2010 5th International Conference on , vol., no., pp.1158-1163, 24-27 Aug. 2010
4 Li, F., Li, D., Wei, Y., Daokun, M., Ding, Q., "Dissolved oxygen prediction in apostichopus japonicus aquaculture ponds by BP neural network and AR model", (2010) Sensor Letters, 8 (1), pp. 95-101.
5 A. Papasalouros, K. Kotis, N. Nikitakos, Towards an intelligent tutoring system for environmental decision makers, Environmental Engineering and Management Journal, February 2010, Vol.9, No.2, pp. 171 292.
6 Francisco Martinez-Alvarez, Alicia Troncoso, Antonio Morales-Esteban, Jose C. Riquelme, "Mineria de datos aplicada a la prediccion de terremotos", CAEPIA XIV Conferencia de la Asociacion Espanola para la Inteligencia Artificial. I Workshop International on Time Series, 2011.
7 Francisco Martinez Alvarez, Alicia Troncoso Lora, Antonio Morales Esteban, Jose C. Riquelme Santos, Computational intelligence techniques for predicting earthquakes, International Conference on Hybrid Artificial Intelligent Systems (HAIS'11), Lecture Notes in Artificial Intelligence, Vol. 6679, No. 2, pages 287-294.
8 Shahriar, Md. Sumon; D'Este, Claire ; Rahman, Ashfaqur, On detecting and predicting harmful algal blooms in coastal information systems, OCEANS, 2012 Yeosu, 21-24 May 2012, pp. 1-3.
9 O'Mara, A; Shahriar, M.S., "Fuzzy clustering-based prediction of marine sensor data," Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on , vol., no., pp.364,368, 23-25 July 2013, DOI: 10.1109/FSKD.2013.6816223
10 Ashfaqur Rahman and MD Sumon Shahriar, Algae Growth Prediction through Identification of Influential Environmental Variables: A Machine Learning Approach, International Journal of Computational Intelligence and Applications, 2013, 12(2), p. 1350008, DOI:10.1142/S1469026813500089
11 Clementking, A., Jothi Venkateswaran, C., Temporal analysis of water quality factors, (2014) International Journal of Applied Engineering Research, 9 (22), pp. 15627-15633.
12 Yang, Y., Tai, H., Li, D., Real-time optimized prediction model for dissolved oxygen in crab aquacul-ture ponds using back propagation neural network, (2014) Sensor Letters, 12 (3-5), pp. 723-729.
13 Androniki Tamvakis, Vasilis Trygonis, John Miritzis, George Tsirtsis, Sofie Spatharis, Optimizing biodiversity prediction from abiotic parameters, Environmental Modelling & Software, Volume 53, March 2014, Pages 112-120, ISSN 1364-8152, http://dx.doi.org/10.1016/j.envsoft.2013.12.001.