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dc.contributor.authorWhigham, Peter Aen_NZ
dc.contributor.authorRecknagel, Friedrichen_NZ
dc.date.available2011-04-07T03:06:02Z
dc.date.copyright1999-09en_NZ
dc.identifier.citationWhigham, P. A., & Recknagel, F. (1999). Predictive modelling of plankton dynamics in freshwater lakes using genetic programming (Information Science Discussion Papers Series No. 99/22). University of Otago. Retrieved from http://hdl.handle.net/10523/1004en
dc.identifier.urihttp://hdl.handle.net/10523/1004
dc.description.abstractBuilding predictive time series models for freshwater systems is important both for understanding the dynamics of these natural systems and in the development of decision support and management software. This work describes the application of a machine learning technique, namely genetic programming (GP), to the prediction of chlorophyll-a. The system endeavoured to evolve several mathematical time series equations, based on limnological and climate variables, which could predict the dynamics of chlorophyll-a on unseen data. The predictive accuracy of the genetic programming approach was compared with an artificial neural network and a deterministic algal growth model. The GP system evolved some solutions which were improvements over the neural network and showed that the transparent nature of the solutions may allow inferences about underlying processes to be made. This work demonstrates that non-linear processes in natural systems may be successfully modelled through the use of machine learning techniques. Further, it shows that genetic programming may be used as a tool for exploring the driving processes underlying freshwater system dynamics.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titlePredictive modelling of plankton dynamics in freshwater lakes using genetic programmingen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages8en_NZ
otago.date.accession2010-11-12 03:23:29en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints1005en_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.referencesBobbin, J. and F. Recknagel, 1999. Mining water quality time series for predictive rules for algal blooms by genetic algorithms. Proc. of the Int. Conference MODSIM 99 (in press). Gruau, F. 1996. On using Syntactic Constraints with Genetic Programming. In:P. a. K. Angeline, Jr., K.E., (Editor) Advances in Genetic Programming 2. 402-417. Holland, J. H. 1992. Adaptation in Natural and Artificial Systems. Cambridge, Mass.: MIT Press Koza, J. R. 1990. Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm. In:H. P. a. M. Schwefel, R., (Editor) Parallel Problem Solving from Nature. 124-129. Koza, J. R. 1992. Genetic Programming:on the programming of computers by means of natural selection. Cambridge, Mass.:MIT Press McKay, R. I., Pearson, R.A. and Whigham, P.A. 1997. Learning Spatial Relationships: Some Approaches. In GeoComputation ’97. R. T. Pascoe, (Editor), University of Otago, Dunedin, New Zealand. 69-79. Recknagel, F. 1997. ANNA - Artificial Neural Network model for predicting species abundance and succession of blue-green algae. Hydrobiologia. 394:47-57. Recknagel, F., and J. Benndorf. 1982. Validation of the ecological simulation model SALMO. Int. Revue ges .Hydrobiol. 67:113-125. Recknagel, F., T. Fukushima, T. Hanazato, N. Takamura, and H. Wilson. 1998. Modelling and Prediction of Phyto- and Zooplankton Dynamics in Lake Kasumigaura by Artificial Neural Networks. Lakes and Reservoirs: Research and Management. 3:123-133. Recknagel, F., and H. Wilson. 1999. Elucidation and prediction of aquatic ecosystems by artificial neural networks. Ecological Modelling. (in press). Reynolds, C. S. 1984. The ecology of freshwater phytoplankton. Press Syndicate of the University of Cambridge, New York Roston, G., and R. Sturges. 1995. A Genetic Design Methodology for Stucture Configuration. ASME Advances in Design Automation. DE 82:73-90. Whigham, P. A., Crapper, P.F. 1999. Time series modelling using genetic programming: An application to rainfall-runoff models. In:L. Spector, Langdon, W.B., O’Reilly, U. and Angeline, P.J., (Editor) Advances in Genetic Programming 3. . MIT Press, Cambridge, MA, USA. 89-104.en_NZ
otago.relation.number99/22en_NZ
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