Automata networks as preprocessing technique of artificial neural network in estimating primary production and dominating phytoplankton levels in a reservoir: An experimental work

Ecological Informatics

Volumn 1, Issue 4, 2006, Pages 431-439

  Kiliç, Hürevren ^a   Soyupak, Selçuk ^a   Gürbüz, Hasan ^b   Kivrak, Ersin ^b  

^a ATILIM UNIVERSITY   (Turkey)

^b ATATÜRK UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Automata networks; Dominating species; Primary production; Principal component analysis

Indexed keywords

ALGA; ARTIFICIAL NEURAL NETWORK; CHLOROPHYLL A; DATA SET; ESTIMATION METHOD; EXPERIMENTAL STUDY; NETWORK ANALYSIS; PHYTOPLANKTON; PRIMARY PRODUCTION; RESERVOIR; TECHNOLOGICAL CHANGE;

DEMIRDOVEN DAM RESERVOIR; ERZURUM [TURKEY]; EURASIA; TURKEY;

CYCLOTELLA OCELLATA; SPHAEROCYSTIS SCHROETERI; STAURASTRUM LONGIRADIATUM;

EID: 37849186103     PISSN: 15749541     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecoinf.2006.09.002     Document Type: Article

References (28)

1. Adamatzky A. Identification of Cellular Automata (1994), Taylor & Francis, London, UK 337 pp.
2. APHA, AWWA and WCPF, 1995. Standard methods for the examination of water and wastewater, 19th ed., APHA, AWWA and WCPF, Washington DC.
3. Aspray W., and Burks A.W. Papers of John Von Neumann on Computing and Computer Theory (1987), MIT Press, Cambridge Mass 640 pp.
4. Chatain T., Helouet L., and Jard C. From automata networks to HMSCs: A reverse model engineering perspective. Lecture Notes in Computer Science 3731 (2005) 489-502
5. Demuth H., and Beale M. Neural Network Toolbox User's Guide Version 7 (2004), The MathWorks Inc., Natick, MA
6. Forster K. Das Phytoplankton des Süβwassers, Systematik und Biologie, Teil 8. In: Huber-Pestalozzi G. (Ed). Conjugatophyceae (Grünalgen), Ordnung Zygnematales und Desmidiales (1982), E. Schweizerbarthsche Verlagsbuchhandlung (Nägele u. Obermiller), Stuttgart (in German)
7. Goles E., and Martinez S. Neural and Automata Networks (1990), Kluwer, Norwell M.A.
8. Guler M., and Kilic H. Built-in self-test generator design using non-uniform cellular automata model. IEE Proceedings. Circuits, Devices and Systems 145 3 (1998) 155-161
9. Gusak O., Dayar T., and Fourneau J.M. Iterative disaggregation for a class of lumpable discrete-time stochastic automata networks. Performance Evaluation 53 1 (2003) 43-69
10. Gürbüz H., Kivrak E., Soyupak S., and Yerli S. Predicting dominant phytoplankton quantities in a reservoir by using neural networks. Hydrobiologia 504 (2003) 133-141
11. Hagan M.T., and Menjah M. Training feed-forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5 6 (1994) 989-993
12. Huber-Pestalozzi G. Das Phytoplankton des Süßwassers, Systematik und Biologie, Teil 5. In: Thienemann A. (Ed). Chlorophyceae (Grünalgen), Ordnung Volvocales (1961), E. Schweizerbarth'sche Verlagsbuchhandlung (Nägele u. Obermiller), Stuttgart (in German)
13. Ideker T.E., Thorsson V., and Karp R.M. Discovery of regulatory interactions through perturbation: inference and experimental design. Proceedings of Pacific Symposium on Biocomputing 5 (2000) 302-313
14. Johnson R., and Wichern D. Applied multivariate statistical methods. Third edition (1992), Prentice Hall, New Jersey
15. Karul C., Soyupak S., and Yurteri C. Neural network models as a management tool in lakes. Hydrobiologia 408/409 (1999) 139-144
16. Kilic H., and Oktem L. Low-power test pattern generator design for BIST via non-uniform cellular automata. Proceedings of IEEE VLSI-TSA International Symposium on VLSI Design, Automation, and Test (IEEE VLSI-TSA-DAT), Hsinchu, Taiwan (2005) 213-216
17. Kivrak E., and Gürbüz H. Seasonal variations in phytoplankton composition and physical-chemical features of Demirdöven Dam Reservoir, Erzurum, Turkey. Biologia (Bratislava) 60/1 (2005) 1-9
18. Komarek J., and Fott B. Das Phytoplankton des Süβwassers, Systematik und Biologie, Teil 7. In: Huber-Pestalozzi G. (Ed). Chlorophyceae (Grünalgen), Ordnung Chlorococcales (1983), E. Schweizerbarthsche Verlagsbuchhandlung (Nägele u. Obermiller), Stuttgart (in German)
19. Lund J.W.G., Kipling C., and Gren E.D. The inverted microscope method of estimating algal numbers and the statistical basis of estimations by counting. Hydrobiologia 11 (1958) 143-170
20. Parson T.R., and Strickland J.D.H. Discussion of spectrophotometric determination of marine plant pigments with revised equations for ascertaining chlorofills and carotenoid. Journal of Marine Research 21 (1963) 155-163
21. Patrick R., and Reimer C.W. The Diatoms of the United States. Acad. Sci. Philadelphia, Monarg. vol. I (1966)
22. Patrick R., and Reimer C.W. The Diatoms of the United States. Acad., Sci, Philadelphia, Monarg. vol. II (1975)
23. Recknagel F., French M., Harkonen P., and Yabunaka K. Artificial neural network approach for modeling and prediction of algal blooms. Ecological Modelling 96 (1997) 11-28
24. Reynolds C.S., Huszar V., Kruk C., Naselli-Flores L., and Melo S. Review towards a functional classification of the freshwater phytoplankton. Journal of Plankton Research 24 (2002) 417-428
25. Ryding S.O., and Rast W. The Control of Eutrophication of Lakes and Reservoirs (1989), Parthenon Publishing Co., UNESCO, Park Ridge, New Jersey
26. Vollenweider R.A., and Kerekes J.J. Appendix I: Background and summary results of the OECD cooperative program on Eutrophication. In: Janus L.L., and Vollenweider R.A. (Eds). The OECD Cooperative Programme on Eutrophication. Canadian Contribution Scientific Series vol. 131 (1981), Environment Canada, Ottawa
27. Voutsadakis G. Threshold agent networks: an approach to modeling and simulation. Applied Mathematics and Computation 142 2-3 (2003) 521-543
28. Xu Z.B., Jin H.D., Leung K.S., Leung Y., and Wong C.K. An automata network for performing combinatorial optimization. Neurocomputing 47 (2002) 59-83