Statistical ecology: a primer on methods & computing
Statistical ecology: a primer on methods & computing
Instance-Based Learning Algorithms
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Machine Learning Methods for Ecological Applications
Machine Learning Methods for Ecological Applications
2006 Special issue: Modular learning models in forecasting natural phenomena
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Uncertainty in the environmental modelling process - A framework and guidance
Environmental Modelling & Software
Environmental Modelling & Software
An empirical study on sea water quality prediction
Knowledge-Based Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Expert Systems with Applications: An International Journal
Application of machine learning methods to spatial interpolation of environmental variables
Environmental Modelling & Software
Protein classification with multiple algorithms
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural Networks
Integrated environmental modeling: A vision and roadmap for the future
Environmental Modelling & Software
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An integrated methodology is proposed for the effective prediction of biodiversity exclusively from abiotic parameters. Phytoplankton biodiversity was expressed as richness, evenness and dominance indices and abiotic parameters included temperature, salinity, dissolved inorganic nitrogen and phosphates. Prediction was based on three machine learning techniques: model trees, multilayer perceptron and instance based learning. To optimize diversity prediction, indices were calculated on a large number of phytoplankton field assemblages, but also on corresponding noise-free simulated assemblages. Biodiversity was most accurately predicted by the instance based learning algorithm and the efficiency was doubled with simulated assemblages. Based on the optimal algorithm, indices, and dataset, a software package was developed for phytoplankton diversity prediction for Eastern Mediterranean waters. The proposed methodology can be adapted to any group of organisms in marine and terrestrial ecosystems whereas important applications are the integration of community structure in ecological models and in assessments of global change scenarios.