from Satellite Data

ocelotgiantΤεχνίτη Νοημοσύνη και Ρομποτική

7 Νοε 2013 (πριν από 4 χρόνια και 6 μέρες)

88 εμφανίσεις

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

Overview


Introduction


Work Detail


Knowledge Acquisition


Knowledge Representation


Bayesian Learning and Inference


Topic Maps



Introduction


Aim to predict plankton primary
production using satellite data


Daily satellite data on surface
temperature, chlorophyll, winds, currents


Archive of ships’ sub
-
surface details


Predict likely subsurface plankton profile
from surface features


Current System


Currently best solution uses Self
Organising Maps (SOMs: A type of neural
network) to classify data


Resulting solution lacks accuracy


Difficult to interpret

Proposed System


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


Work Detail

Knowledge

Acquisition

Inference Engine

Knowledge

Representation

Learning Engine

Topic Map

Requirements

Elicitation

Rob Curtis

Richard Fenn

Damon Oberholster

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”

www.centc251.org/Ginfo/Glossary/tcglosk.htm


Relating to this project:


Huge amounts of data


Data is poorly recorded in Excel spreadsheets


Gaps in current data

Knowledge Acquisition: Amount of Data


2500 ship sub surface readings


Recorded over 10 year 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

Knowledge Acquisition: Current Data


Knowledge Acquisition: Gaps in Data

Ships’ sub
-
surface readings
(discrete)

Satellite data (continuous)

Knowledge Acquisition: Gaps in Data

Knowledge Acquisition: Challenges


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


Representing all the data in a form easily used
by the Bayesian Network


Integration of disparate data



Knowledge Representation



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:


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




Knowledge Representation: Causal
Relationships

Primary


Plankton


Production

Many variables that influence plankton production:


Chlorophyll


Surface Temp


Wind


Current

Chlorophyll

Surface Temp

Wind

Knowledge Representation:
Bayesian Network


Directed graphical model


Each node represents influencing variable


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

Knowledge Representation:
Temporal aspects




Need to divide data up into time steps


Each time step is dependant on previous step

t + 1

t

t + 2

Learning Engine


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


nine years of satellite and ship data will be
used in training the system

Inference Engine


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

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.




Representing Bayesian
Networks using Topic Maps

Topic Maps: Overview


Brief introduction to topic maps and
hypergraphs


Applying topic maps to the system


Testing


Challenges

Topic Maps


Topic maps provide means for indexing
data


ISO standard for describing knowledge
structures and associating them with
information resources.

Topic Map Structure


Topic


Anything, subject, entity, concept


Occurrence


Link to information about topic


Association


Relationships between topics

Topic Map Structure

Occurrence

Topic

Association

Representing Topic Maps


Hypergraphs


hypergraph is a graph that can have smaller graphs
(subgraphs) imbedded within itself











Applying Topic Maps


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


Web application linking topic maps for
different regions of the ocean


Testing


Qualitative approach



Low
-
Fi prototypes to test intuitiveness of
proposed interface to Bayesian Network



Test with the intended users of the system


Challenges


Representing temporal information using
topic maps


Representing Bayesian Network
relationships using topic maps


SUMMARY


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 topic
maps for intuitively representing
Bayesian Networks


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