An inductive approach to ecological time series modelling by evolutionary computation

Ecological Modelling

Volumn 146, Issue 1-3, 2001, Pages 275-287

  Whigham, P A ^a   Recknagel, Friedrich ^b  

^a UNIVERSITY OF OTAGO   (New Zealand)

^b UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Chlorophyll a modelling; Genetic programming; Machine learning; Time series modelling

Indexed keywords

CHLOROPHYLL A; ECOLOGICAL MODELING; GENETIC ALGORITHM; TIME SERIES;

EID: 0035669679     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3800(01)00313-1     Document Type: Article

References (23)

1. Min Ing water quality time series for predictive rules of algal blooms by genetic algorithm
2. Modelling freshwater phytoplankton communities: An exercise in validation
3. Relevance feedback retrieval of time series data
4. Concept formation and decision tree induction using the genetic programming paradigm
5. Time series prediction by using negatively correlated neural networks
6. Learning spatial relationships: Some approaches
7. Validation of the ecological simulation model SALMO
8. Multivariate time series modelling of algal blooms in freshwater lakes by machine learning
9. Aritificial neural network approach for modelling and prediction of algal blooms
10. Modelling and prediction of Phyto- and Zooplankton dynamics in Lake Kasumigaura by artificial neural networks
11. Blue-green algae: Why they become dominant
12. Declarative bias in equation discovery
13. Modelling and prediction of phytoplankton growth with equation discovery
14. Inductive bias and genetic programming
15. Induction of a marsupial density model using genetic programming and spatial relationships
16. Time series modelling using genetic programming: An application to rainfall-runoff models
17. Evolving difference equations to model freshwater phytoplankton
18. Predictive modelling of plankton dynamics in freshwater lakes using genetic programming