1. Decision Support Tools for River Quality Management
Martin Paisley, David Trigg and William Walley
Centre for Intelligent Environmental Systems, Faculty of Computing, Engineering & Technology, Staffordshire University

2. Contents Background The River Pollution Diagnostic System (RPDS).
Our Aims
Our Approach
The River Pollution Diagnostic System (RPDS).
Pattern Recognition
Data exploration, diagnosis and classification
The River Bayesian Belief Network (RPBBN).
Plausible Reasoning
Diagnosis, prognosis and scenario-testing
Summary
© 2009 David Trigg

3. Our Aims
Maximise the benefit gained from existing databases/information, increased objectivity.
Exploit the available technology to create sophisticated, flexible, multi-purpose tools
Make the technology easy to use.
Provide expert support to those who need it to help them do their job.
© 2009 David Trigg

4. Our Approach
Our initial studies with expert ecologist H.A. Hawkes lead to goal of trying to capture expertise.
Expert systems is the branch of Artificial Intelligence (AI) that attempts to capture expertise in a computer based system.
Study of an expert is required to reveal:
what they do,
how they do it; and
what information and mental processes they use.
© 2009 David Trigg

5. The Expert Ecologist
Our early research discovered the expert ecologist tend used to use two complementary techniques.
Memory (pattern matching) – “I’ve seen this before, it was due to …”
Scientific knowledge (plausible reasoning) – based on their knowledge of the system and available evidence they are able to reason about the likely state of other elements of the system.
We set out to replicate these processes and produce software that would allow people to gain easy access to ‘expert’ interpretation
© 2009 David Trigg

6. The Modelling Tools
After over a decade of research in this field the current modelling techniques we use are:
our own clustering and visualisation system know as MIR-Max (Mutual Information & Regression Maximisation) for pattern matching; and
Bayesian Belief Networks (BBN) for plausible reasoning.
These techniques were used to produce the models on which our decision support software is based.
© 2009 David Trigg

7. What the tools provide.
Visualisation and exploration of large complex datasets. (RPDS)
Classification of samples. (RPDS)
Diagnosis of potential pressures. (RPDS & RPBBN)
Prediction of biology from environmental and chemical parameters. (RPBBN)
Scenario testing – impact of changing sample parameters. (RPBBN)
© 2009 David Trigg

8. Pattern Recognition
© 2009 David Trigg

9. Pattern Recognition –What is it?
Recognition of patterns – pattern implies multiple attributes, so is a multivariate technique.
Classification of a new pattern (thing) as being of a particular type, based on similarity to a set of attributes indicative of that type.
Success of pattern recognition reliant on having the appropriate distinguishing features.
Enough features to clearly discriminate.
Appropriate set of features – orthogonal/uncorrelated.
© 2009 David Trigg

10. Pattern Recognition – Why do it?
Method of managing information – reduce multiple instances as single type or kind.
Classification of situations allows to cope with novel but similar situations.
Exploitation of existing ‘information’. Once identified as being of a type ‘unknown’ attributes can be inferred.
© 2009 David Trigg

11. Pattern Recognition - Clustering
To create a model first need to cluster training samples
The training samples contain both data on the training/clustering variables and additional ‘information’ variables (those that are to be predicted).
In the case of RPDS, the training variables are the biology and the information variables the chemical and other stress parameters.
© 2009 David Trigg

12. Pattern Recognition - Clustering
Set of samples .. grouped into ‘clusters’ .. to provide templates/types in the model
© 2009 David Trigg

13. Pattern Recognition - Classification
Classification involves matching a new sample with an existing cluster. Based on the training variables.
In this example the closest match for the new sample is cluster ‘A’.
This is the ‘classification’ of the new sample. The quality of the cluster is that assigned to the new sample.
© 2009 David Trigg

14. Pattern Recognition - Diagnosis
The diagnosis is derived from the values for the information variables (the blue bars) in the training samples grouped in the cluster.
The predicted values are derived from the training samples in the cluster.
These values are usually a statistic such as mean, median or a percentile.
© 2009 David Trigg

15. Visualisation Classification can appear as a black box system.
Visualisation is a useful tool.
Opens the model up for inspection.
Helps understand & validate model.
Helps explore data and discovery of new relationships.
To help visualisation clusters can be ‘ordered’ in a map.
© 2009 David Trigg

16. Ordering
Ordering sole purpose is to help visualise the data and the cluster model, no more no less.
The process involves arranging the clusters in a space/map usually based on similarity. Similar clusters are placed close together dissimilar far apart.
Our algorithm, R-Max, uses the r correlation coefficient between distances in data space and corresponding distances in output space
© 2009 David Trigg

17. Data Visualisation - Ordering Clustersj d x y z X Y D j i
d = distance in data space D = distance between clusters in map R-Max aims to maximise the correlation r between d and D
© 2009 David Trigg

18. Pattern Recognition - Ordering
Clusters templates/types … destination map … clusters ordered by similarity
© 2009 David Trigg

19. Pattern Recognition - Visualisation
Maps can be colour-coded to show the value of any chosen feature across all of the clusters
‘Feature maps’ and ‘templates’ form the basis of RPDS visualisation
© 2009 David Trigg

20. RPDS 3.0
Primary uses are
Data exploration – visual element to the clustered/organised data allows existing relationships in the data to be verified (model validation) and new ones to be identified (data mining).
Classification - assignment of a sample to cluster allows an estimated quality class to be defined.
Diagnosis - The ‘known’ stress information associated with other samples in the cluster can help diagnose potential problems.
© 2009 David Trigg

21. RPDS 3.0 - Data Exploration
© 2009 David Trigg

22. RPDS 3.0 - Data Exploration
© 2009 David Trigg

23. RPDS 3.0 - Data Exploration
© 2009 David Trigg

24. RPDS 3.0 - Data Exploration
© 2009 David Trigg

25. RPDS 3.0 - Data Exploration
© 2009 David Trigg

26. RPDS Classification
© 2009 David Trigg

27. RPDS Classification
© 2009 David Trigg

28. RPDS Diagnosis
© 2009 David Trigg

29. RPDS Comparison
© 2009 David Trigg

30. Plausible Reasoning
© 2009 David Trigg

31. Reasoning
Reasoning:
Thinking that is coherent and logical.
A set of cognitive processes by which an individual may infer a conclusion from an assortment of evidence or from statements of principles.
Goal-directed thought that involves manipulating information to draw conclusions of various kinds.
Use available information combined with existing knowledge to derive conclusions for a particular purpose.
© 2009 David Trigg

32. Reasoning with Uncertainty
If reasoning is ‘coherent and logical’, how can it deal with unknowns, conflicting information and uncertainty?
The ability to quantifying uncertainty helps to resolve conflicts and provides ‘lubrication’ for the reasoning process.
In humans this takes the form of beliefs.
Probability theory provides a mathematical method of handling uncertainty.
© 2009 David Trigg

33. Probability Theory
Probability theory is robust and proven to be a mathematically sound.
It provides a method for representing and manipulating uncertainty.
It is one of the principle methods used for handling uncertainty in computer based systems.
Bayesian Belief Networks (BBN) are currently the most popular methods for creating probabilistic systems.
© 2009 David Trigg

34. Bayesian Belief Networks
A BBN consists of two elements causal network and a set of probability matrices.
A causal network is a graph of arcs (variables) and directed edges (relationships).
The network defines the relationships between all the variables in a domain.
The causal variables are often referred to ‘parents’ and the effect variables as ‘children’.
Can be defined through data analysis but is probably best achieved by an expert.
© 2009 David Trigg

35. Causal Network
© 2009 David Trigg

36. Probability Matrix
The probability matrices encode the relationship between variables.
A probability is required for every combination of parent and child states.
The number of states grows geometrically meaning that the derivation probabilities is often better achieved via data analysis.
© 2009 David Trigg

37. Probability Values (0 - 100)
Outputs - Predictions
The outputs of the system are likelihood of each of the states of the variables occurring.
The whole system is updated every time evidence is entered regardless of where it occurs.
The most common way to represent the values is through a bar chart, where the bars depict the likelihood of each state.
Variable Name
State Labels
Probability Bars
Probability Values ( )
© 2009 David Trigg

38. RPBBN 2.0 Primary uses are:
Prediction of concentrations of common ‘chemical’ pollutants from biological sample data.
Scenario testing, prediction of new biological community and biological assessment ‘scores’ based on the modification of changeable environmental and chemical parameters for a site.
© 2009 David Trigg

39. RPBBN Prediction
© 2009 David Trigg

40. RPBBN Prediction
© 2009 David Trigg

41. RPBBN 2.0 - Scenario Testing
© 2009 David Trigg

42. RPBBN 2.0 - Scenario Testing
© 2009 David Trigg

43. Summary
RPDS organises the EA dataset allowing exploration and analysis and provides the ability to classify new samples and diagnose potential problems.
RPBBN allows prediction of the states of variables in a system based on any available evidence. Making it useful for diagnosis, prognosis and scenario testing.
Together these tools can help decision makers identify potential problems, suggest areas for further investigation, help develop programmes of remedial action and define targets.
© 2009 David Trigg

44. Summary
The models are based primarily on data analysis making them more objective than expert opinion.
The systems robust and consistent in their operation.
The software is easily reproduce and distributed meaning that the valuable expertise they hold can easily be spread through out an organisation
© 2009 David Trigg

45. The Future
River Quality - include more geographic information and move from site to river basin management.
Improvement in algorithms, incorporation of sample bias and improved confidence measures.
Major revision of software – potentially rewritten as web-based application.
© 2009 David Trigg