1. Using Bayesian Networks to Predict Plankton Production from Satellite Data By: Rob Curtis, Richard Fenn, Damon Oberholster Supervisors: Anet Potgieter, John Field, Laurent Drapeau Department of Computer Science

2. Overview Introduction Introduction Work Detail Work Detail Knowledge Acquisition Knowledge Acquisition Knowledge Representation Knowledge Representation Bayesian Learning and Inference Bayesian Learning and Inference Topic Maps Topic Maps

3. Introduction Aim to predict plankton primary production using satellite data Aim to predict plankton primary production using satellite data Daily satellite data on surface temperature, chlorophyll, winds, currents Daily satellite data on surface temperature, chlorophyll, winds, currents Archive of ships’ sub-surface details Archive of ships’ sub-surface details Predict likely subsurface plankton profile from surface features Predict likely subsurface plankton profile from surface features

4. Current System Currently best solution uses Self Organising Maps (SOMs: A type of neural network) to classify data Currently best solution uses Self Organising Maps (SOMs: A type of neural network) to classify data –Resulting solution lacks accuracy –Difficult to interpret

5. Proposed System Propose a system that uses Bayesian Networks to predict plankton production Propose a system that uses Bayesian Networks to predict plankton production –Use ships’ sub surface profiles + satellite data to draw cause effect relationships –Will use Bayesian Inference and Learning Use Topic Maps to visualize network Use Topic Maps to visualize network

6. Work Detail Knowledge Acquisition Inference Engine Knowledge Representation Learning Engine Topic Map Requirements Elicitation Rob Curtis Richard Fenn Damon Oberholster

7. Knowledge Acquisition “The process of analyzing, transforming, classifying, organizing and integrating knowledge and representing that knowledge in a form that can be used in a computer system. Typically the knowledge is based on what a human expert does when solving problems” “The process of analyzing, transforming, classifying, organizing and integrating knowledge and representing that knowledge in a form that can be used in a computer system. Typically the knowledge is based on what a human expert does when solving problems”www.centc251.org/Ginfo/Glossary/tcglosk.htm Relating to this project: Relating to this project: –Huge amounts of data –Data is poorly recorded in Excel spreadsheets –Gaps in current data

8. Knowledge Acquisition: Amount of Data 2500 ship sub surface readings 2500 ship sub surface readings –Recorded over 10 year period Bayesian Network requires satellite data for the same time period Bayesian Network requires satellite data for the same time period Need to represent data in a form that can be used by the Bayesian Network Need to represent data in a form that can be used by the Bayesian Network

9. Knowledge Acquisition: Current Data

10. Knowledge Acquisition: Gaps in Data Ships’ sub-surface readings (discrete) Satellite data (continuous)

11. Knowledge Acquisition: Gaps in Data

12. Knowledge Acquisition: Challenges Making sense of all the available data (consultations with Dr John Field and Laurent Drapeau) Making sense of all the available data (consultations with Dr John Field and Laurent Drapeau) Correlating the 2D continuous satellite data to 3D discrete ships’ sub-surface profile Correlating the 2D continuous satellite data to 3D discrete ships’ sub-surface profile Representing all the data in a form easily used by the Bayesian Network Representing all the data in a form easily used by the Bayesian Network Integration of disparate data Integration of disparate data

13. Knowledge Representation “ A search for formal ways to describe knowledge presented in informal terms (a prerequisite for its handling as computation)” “ A search for formal ways to describe knowledge presented in informal terms (a prerequisite for its handling as computation)”encyclopedia.laborlawtalk.com/Representation Relating to this project: Relating to this project: –Need to find causal relationships between environment variables –Represent those relationships in a Bayesian Network –Store the data in a database so that it will be easy for the Inference and Learning Engines of the Bayesian Network to Manipulate. –Need to consider the temporal aspects of the data

14. Knowledge Representation: Causal Relationships Primary Plankton Production Many variables that influence plankton production: Chlorophyll Surface Temp Wind Current Chlorophyll Surface Temp Wind

15. Knowledge Representation: Bayesian Network Directed graphical model Directed graphical model Each node represents influencing variable Each node represents influencing variable An edge from one node to another represents causal relationship between those nodes An edge from one node to another represents causal relationship between those nodes Create Bayesian network structure based on the most relevant relationships found between the variable Create Bayesian network structure based on the most relevant relationships found between the variable

16. Knowledge Representation: Temporal aspects Need to divide data up into time steps Each time step is dependant on previous step t + 1tt + 2

17. Learning Engine Each Node of the Bayesian network will have a Conditional Probability Table (CPT) Each Node of the Bayesian network will have a Conditional Probability Table (CPT) Learning engine will implement an algorithm to update the probabilities in each of these tables Learning engine will implement an algorithm to update the probabilities in each of these tables –nine years of satellite and ship data will be used in training the system

18. Inference Engine The inference engine will be responsible for calculating the probability of a certain sequence of observations given certain input parameters The inference engine will be responsible for calculating the probability of a certain sequence of observations given certain input parameters

19. Testing Nine years of sub-surface data will be used to train the system. Compare the predicted results for the tenth year against the recorded results for that year. The project will be a success if predictions are very similar to those that were recorded.

20. Representing Bayesian Networks using Topic Maps

21. Topic Maps: Overview Brief introduction to topic maps and hypergraphs Brief introduction to topic maps and hypergraphs Applying topic maps to the system Applying topic maps to the system Testing Testing Challenges Challenges

22. Topic Maps Topic maps provide means for indexing data Topic maps provide means for indexing data ISO standard for describing knowledge structures and associating them with information resources. ISO standard for describing knowledge structures and associating them with information resources.

23. Topic Map Structure Topic Topic –Anything, subject, entity, concept Occurrence Occurrence –Link to information about topic Association Association –Relationships between topics

24. Topic Map Structure Occurrence Topic Association

25. Representing Topic Maps Hypergraphs Hypergraphs  hypergraph is a graph that can have smaller graphs (subgraphs) imbedded within itself

26. Applying Topic Maps Bayesian Network Bayesian Network –Topics will represent nodes in the network –Associations represent relationships between nodes in the network –Occurrences will link to info about node Future System Future System –Web application linking topic maps for different regions of the ocean

27. Testing Qualitative approach Qualitative approach Low-Fi prototypes to test intuitiveness of proposed interface to Bayesian Network Low-Fi prototypes to test intuitiveness of proposed interface to Bayesian Network Test with the intended users of the system Test with the intended users of the system

28. Challenges Representing temporal information using topic maps Representing temporal information using topic maps Representing Bayesian Network relationships using topic maps Representing Bayesian Network relationships using topic maps

29. SUMMARY Represent data in a formal way using knowledge acquisition and representation Represent data in a formal way using knowledge acquisition and representation Research the viability of using Bayesian Networks as a prediction mechanism Research the viability of using Bayesian Networks as a prediction mechanism Research the viability of using topic maps for intuitively representing Bayesian Networks Research the viability of using topic maps for intuitively representing Bayesian Networks

30. References Pepper, S. (2002), ”The TAO of Topic Maps, Finding the Way in the Age of Infoglut”, retrieved 01/06/2005, URL: http://www.ontopia.net/topicmaps/materi als/tao.html Pepper, S. (2002), ”The TAO of Topic Maps, Finding the Way in the Age of Infoglut”, retrieved 01/06/2005, URL: http://www.ontopia.net/topicmaps/materi als/tao.html http://www.ontopia.net/topicmaps/materi als/tao.html http://www.ontopia.net/topicmaps/materi als/tao.html