Computational assemblage of ordinary differential equations for chlorophyll-a using a lake process equation library and measured data of Lake Kasumigaura

Ecological Informatics: Scope, Techniques and Applications

Volumn , Issue , 2006, Pages 409-427

  Atanasova, N ^a   Recknagel, F ^b   Todorovski, L ^c   Dzeroski S ^c   Kompare, B ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b UNIVERSITY OF ADELAIDE   (Australia)

^c JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79551512215     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/3-540-28426-5_20     Document Type: Chapter

References (21)

1. Atanasova N (2005) Priprava in uporaba ekspertnega predznanja za avtomatizirano modeliranje vodnig ekosistemov. PhD Thesis, University of Ljubljana, Ljubljana, Slovenia
2. Benndorf J, Recknagel F (1982) Problems of application of the ecological model SALMO to lakes and reservoirs having various trophic states. Ecological Modelling 17, 129-145
3. Bobbin J, Recknagel F (2003) Evolving rules for the prediction and explanation of blue-green algal succession in lakes by evolutionary computation. In: Recknagel F (ed.) (2003) Ecological Informatics. Understanding Ecology by Biologically-Inspired Computation. Springer-Verlag Berlin, Heidelberg, New York, 291-310
4. Chapra SC (1997) Surface Water-Quality Modeling: McGraw-Hill
5. DeAngelis DL (1992) Dynamics of Nutrient Cycling and Food Webs. London: Chapmann & Hall
6. Dzeroski S, Todorovski L (2003) Learning population dynamics models from data and domain knowledge. Ecological Modelling, 170(2-3): 129-140.
7. Joergensen SE, Bendoricchio G (2001) Fundamentals of Ecological Modelling, Third Ed. Amsterdam: Elsevier Science Ltd.
8. Kompare B (1995) The Use of Artificial Intelligence in Ecological Modelling. Ph.D. Thesis, FGG, Ljubljana; Royal Danish School of Pharmacy, Copenhagen, Ljubljana, Copenhagen
9. Langley P, Sanchez J, Todorovski L, Dzeroski S (2002) Inducing process models from continuous data. Paper presented at the The Nineteenth International Conference on Machine Learning, Sydney Australia.
10. Recknagel F (1989) Applied Systems Ecology. Approach and Case Studies in Aquatic Ecology. Akademie-Verlag, Berlin, 1-138
11. Recknagel F, Bobbin J, Whigham P, Wilson H (2002) Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling of algal blooms in freshwater lakes. Journal of Hydroinformatics 4, 2, 125-134
12. Recknagel F, Fukushima T, Hanazato T, Takamura N, Wilson H (1998) Modelling and prediction of phyto-and zooplankton dynamics in Lake Kasumigaura by artificial neural networks. Lakes & Reservoirs 3, 123-133
13. Recknagel F (1997) ANNA-Artificial Neural Network model predicting species abundance and succession of blue-green Algae. Hydrobiologia, 349, 47-57
14. Recknagel F, French M, Harkonen P, Yabunaka K (1997) Artificial neural network approach for modelling and prediction of algal blooms. Ecological Modelling 96,1-3,11-28
15. Recknagel F,. Benndorf J (1982) Validation of the ecological simulation model SALMO. Int. Revue ges. Hydrobiol. 67, 1, 113-125
16. Straskraba M, Gnauck A (1985) Freshwater Ecosystems, Modelling and Simulation. Elsevier, Amsterdam
17. Todorovski L (2003) Using Domain Knowledge for Automated Modeling of Dynamic Systems with Equation Discovery. PhD Thesis, University of Ljubljana, Ljubljana, Slovenia
18. Todorovski L, Džeroski S (1997) Declarative bias in equation discovery. Paper presented at the 14th International Conference on Machine Learning, San Mateo, CA
19. Vollenweider RA (1968) The scientific basis of lake and stream eutrophication with particular reference to phosphorus and nitrogen as eutrophication factors. Paris: Organisation for Economic Cooperation and Development.
20. Wei B, Sugiura N, Maekawa T (2001) Use of artificial neural networks in the prediction of algal blooms. Water Research, 35(8): 2022-2028
21. Whigham P, Recknagel F (2001) Predicting Chlorophyll-a in Freshwater Lakes by Hybridising Process-Based Models and Genetic Algorithms. Ecol. Modelling 146,1-3,243-251