This course will cover a range of computational methods applicable for
the analysis of toxicants and environmental sciences’ data.
• Methods to pattern and cluster biota community according to the
• Modelling approaches (e.g. artificial neural networks, classification
models) to predict aquatic ecosystem quality by using the
responses of biota community to changes of the environment. Two
types of algorithm will be presented with relevant application
• unsupervised learning models (ordination and clustering)
used for patterning communities according to the effects of
• supervised learning models (predictive models) used to
predict community disturbances by anthropogenic impacts,
especially toxic substances.
• The use of sensitivity analysis techniques to illustrate the
relationships between toxicants and biota.
• Modelling of structures (e.g. spatial, temporal) using Moran's
eigenvector maps and asymmetric eigenvector maps.
• Analysis of multiple scale correlative features using the Multiscale
The methods will be applied on large datasets. The participants will be
given practical examples using the R statistical software.