lakshman@ou.edu
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Data

driven methods in
Environmental Sciences
Exploration of Artificial Intelligence Techniques
Valliappa.Lakshmanan@noaa.gov
lakshman@ou.edu
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Data Driven Methods
What is Artificial Intelligence?
Common AI techniques
Choosing between AI techniques
Pre and post processing
lakshman@ou.edu
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What is AI?
Machines that perceive, understand and
react to their environment
Goal of Babbage, etc.
Oldest endeavor in computer science
Machines that think
Robots: factory floors, home vacuums
Still quite impractical
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AI vs. humans
AI applications built on Aristotlean logic
Induction, semantic queries, system of logic
Human reasoning involves more than just induction
Computers never as good as humans
In reasoning and making sense of data
In obtaining a holistic view of a system
Computers much better than humans
In processing reams of data
Performing complex calculations
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Successful AI applications
Targeted tasks more amenable to
automated methods
Build special

purpose AI systems
Determine appropriate dosage for a drug
Classify cells as benign or cancerous
Called “expert systems”
Methodology based on expert reasoning
Quick and objective ways to obtain answers
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Data Driven Methods
What is Artificial Intelligence?
Common AI techniques
Choosing between AI techniques
Pre and post processing
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Fuzzy logic
Fuzzy logic addresses key problem in
expert systems
How to represent domain knowledge
Humans use imprecisely calibrated terms
How to build decision trees on imprecise
thresholds
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Fuzzy logic example
Source: Matlab fuzzy logic toolbox tutorial
http://www.mathworks.com/access/helpdesk/help/toolbox/fuzzy/fp350.html
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Advantages of fuzzy logic
Considerable skill for little investment
Fuzzy logic systems piggy bank on human
analysis
Humans encode rules after intelligent analysis of
lots of data
Verbal rules generated by humans are robust
Simple to create
Not much need for data or ground truth
Logic tends to be easy to program
Fuzzy rules are human understandable
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Where not to use fuzzy logic
Do not use fuzzy logic if:
Humans do not understand the system
Different experts disagree
Knowledge can not be expressed with verbal rules
Gut instinct is involved
Not just objective analysis
A fuzzy logic system is limited
Piece

wise linear approximation to a system
Non

linear systems can not be approximated
Many environment applications are non

linear
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Neural Networks
Neural networks can approximate non

linear systems
Evidence

based
Weights chosen through optimization procedure
on known dataset (“training”)
Works even if experts can’t verbalize their
reasoning, or if there is ground truth
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A example neural network
Diagram from:
http://www.codeproject.com/useritems/GA_ANN_XOR.asp
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Advantages of neural networks
Can approximate any smooth function
The three

layer neural network
Can yield true probabilities
If output node is a sigmoid node
Not hard to train
Training process is well understood
Fast in operations
Training is slow, but once trained, the network can
calculate the output for a set of inputs quite fast
Easy to implement
Just a sum of exponential functions
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Disadvantages of neural networks
A black box
The final set of weights yields no insights
Magnitude of weights doesn’t mean much
Measure of skill needs to be differentiable
RMS error, etc.
Can not use Probability of Detection, for example
Training set has to be complete
Unpredictable output on data unlike training
Need lots of data
Need expert willing to do lot of truthing
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Recap:
Fuzzy logic
Humans provide the rules
Not optimal
Neural network
Humans can not understand system
Optimal
Middle ground?
Genetic Algorithms
Decision Trees
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Genetic algorithms
In genetic algorithms
One fixes the model (rule base, equations,
class of functions, etc.)
Optimize the parameters to model on
training data set
Use optimal set of parameters for unknown
cases
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An example genetic algorithm
Sources:
http://tx.technion.ac.il/~edassau/web/genetic_algorithms.htm
http://cswww.essex.ac.uk/research/NEC/
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Advantages of genetic algorithms
Near

optimal parameters for given model
Human

understandable rules
Best parameters for them
Cost function need not be differentiable
The process of training uses natural
selection, not gradient descent
Requires less data than a neural network
Search space is more limited
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Disadvantages of genetic algorithms
Highly dependent on class of functions
If poor model is chosen, poor results
Optimization may not help at all
Known model does not always lead to
better understanding
Magnitude of weights, etc. may not be
meaningful if inputs are correlated
Problem may have multiple parametric
solutions
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Decision trees
Can automatically build decision trees from
known data
Prune trees
Select thresholds
Choose operators
Disadvantages
Piece

wise linear, so typically less
skilled than neural networks
Large decision trees are effectively a
blackbox
Can not do regression, only
classification
Advantages:
Fast to train
New advances: bagged, boosted
decision trees approach skill of neural
networks, but are no longer fast to train
Root
30
50
T < 10C
20
15
T > 10C
10
35
Z > 45
18
2
Z < 45
2 13
V < 5
8
2
V > 5
2
33
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Radial Basis Functions
Radial Basis Functions are a form of neural network
Localized gaussians
Linear sum of non

linear functions
Advantage: Can be solved by inverting a matrix, so very fast
Disadvantage: Not a general

enough model
Diagram from: A. W. Jayawardena & D. Achela K. Fernando 1998: Use of Radial Basis Function Type Artificial Neural
Networks for Runoff Simulation, Computer

Aided Civil and Infrastructure Engineering 13:2
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Data Driven Methods
What is Artificial Intelligence?
Common AI techniques
Choosing between AI techniques
Pre and post processing
lakshman@ou.edu
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Typical data

driven application
Input Data
Features
f(features)
Result
How do we find f()
Which features?
AI application
in run

time
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What is the role of the data?
Validation
Test known model
Technique:
Difference between model output and ground truth helps to validate the
model
Calibration
Find parameters to model with desired structure
Technique:
Tuned fuzzy logic method
Genetic algorithms
Induction
Find model and parameters from just data
Technique:
Neural network methods, bagged/boosted decision trees, support vector
machines, etc.
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What is the problem to solve?
Do you have a bunch of data and want to:
Estimate an unknown parameter from it?
True rainfall based on radar observations?
Amount of liquid content from in

situ measurements of
temperature, pressure, etc?
Regression
Classify what the data correspond to?
A water surge?
A temperature inversion?
A boundary?
Classification
Regression and classification aren’t that different
Classification: estimate probability of an event
A function from 0

1
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Which AI technique?
Do you have expert knowledge?
Humans have a “model” in their head? Should the final f() be
understandable?
Create
fuzzy logic
rules from experts’ reasoning
Aggregate the individual fuzzy logic rules
Can tune the fuzzy rules based on data
Using regression, decision trees or neural networks for RMS error criterion
Genetic algorithms for error criteria like ROC, economic cost, etc.
Many times the original rules are just fine
Do you already know the model?
A power

law relationship? Gaussian? Quadratic? Rules?
Just need to find parameters to this model?
If linear, just use linear regression
If non

linear: use
genetic algorithms
Use continuous GAs
Both of these can be used for regression (therefore, also
classification)
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Which AI technique (contd.)
Do you know nothing about the data?
Not the suspected equation/model (GA)?
Not the suspected rules (fuzzy logic)?
Use a AI technique that supplies its equations/rules
“black box”.
For classification, use:
Bagged decision trees or Support Vector Machines
If output is probabilistic, remember to apply Platt scaling
Summary statistics on bagged DTs can help answer “why”
Neural Networks
For regression, use:
Neural networks
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Where do your data come from?
Observed data
Compute features
Choose AI technique
The 4 choices in the previous two slides
Simulated data:
Example: trying to replicate a very complex model
Throw randomly

generated data at model
Compute features
Choose AI technique:
GA for parametric approximations
NN when you don’t know how to approximate
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Where do you get your inputs?
What type of data do you have?
Individual observations?
Sample them (choose at random) and use directly
Sparse observations in a time series?
Generate time

based features (1D moving windows)
Signal processing features from time series
Data from remotely sensed 2D grids?
Generate image

based features using convolution filters
Do you need:
Pixel

based regression/classification?
Use convolution features directly
Object

based regression/classification?
Identify regions using region growing
Use region

aggregate features
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Typical data

driven application
Observed data
Features
f()
Result
Signal/image processing;sampling
normalize/create chromosome/
determine confidences
FzLogic/GenAlg/NN/DecTree
Platt method/region

average/threshold
A data

driven application
in run

time
lakshman@ou.edu
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Data Driven Methods
What is Artificial Intelligence?
Common AI techniques
Choosing between AI techniques
Pre and post processing
lakshman@ou.edu
32
Preprocessing
Often can not use pixel data directly
Too much data, too highly correlated
May need to segment pixels into objects and use features
computed on the objects
Different data sets may not be collocated
Need to interpolate to line them up
Mapping, objective analysis
Noise in data may need to be reduced
Smoothing
Present statistic of data, rather than data itself
Features need to be extracted from data
Human experts often good source of ideas on signatures to
extract from data
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Postprocessing
The output of an expert system may be grid
point by grid point
May need to provide output on objects
Storms, forests, etc.
Can average outputs over objects’ pixels
May need probabilistic output
Scale output of maximum marginal techniques
Use a sigmoid function
Called Platt scaling
lakshman@ou.edu
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Summary
What is Artificial Intelligence?
Data

driven methods to perform specific targeted tasks
Common AI techniques
Fuzzy logic, neural networks, genetic algorithms, decision
trees
Choosing between AI techniques
Understand the role of your data
Do experts understand the system? (have a model)
Do experts expect to understand the system? (readability)
Pre and post processing
Image processing techniques on spatial grids
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