# Presentation: 137407

## Weka:  A Useful Tool for Air Quality Forecasting 

- William F. Ryan
- Department of Meteorology
- The Pennsylvania State University
- wfr1@psu.edu

- 2007 National Air Quality Conference, Orlando

## Weka

- The weka, or woodhen, is a bird
- native to New Zealand.   Weka is
- also the name of a suite of machine
- learning software tools, written in
- Java, and developed at the University
- of Wiakato in New Zealand.

- _[http://www.cs.waikato.ac.nz/ml/weka](http://www.cs.waikato.ac.nz/ml/weka)__[ ](http://www.cs.waikato.ac.nz/ml/weka)_

## Machine Learning

- Machine learning is a subfield of artificial intelligence (AI) concerned with the development of algorithms and techniques that allow computers to "learn".
- The machine learning algorithms in Weka include, among others, linear regression, classification trees, clustering and artificial neural networks (ANN).

## Weka Can Be A Useful Tool

- Weka has the potential to be a useful tool to support local air quality forecasting efforts – particularly those operating on a limited budget.
- Weka is open source (free) software - although the purchase of the associated text book is strongly recommended.
- Weka is easily installed on standard PC's but can also run on Linux and other platforms.
- Only minimal modifications are necessary to prepare data files for use in Weka.
- The user interface is simple and intuitive.

## Weka and PM2.5 Forecasting

- Of particular interest to air quality forecasters is the wide range of algorithms included in Weka.
- These algorithms may be useful to address shortcomings in statistical forecast guidance for fine particulate matter (PM2.5).
- Simple linear regression methods provide reasonable skill for O3 forecasting, due to the very strong and nearly linear ozone-temperature relationship, but linear regression methods have shown limited skill in forecasting PM2.5.

## PM2.5 Forecasting

- O3 (left panel) is well-behaved
- statistically.  Distribution is near
- normal with a strong association
- with maximum temperature. As a
- result, linear techniques are
- useful.
- PM2.5 (right panel) is not well-
- behaved.  Distribution is skewed,
- no strong association with any
- particular weather variable.
- Tools included in Weka, including
- ANN and classification
- and regression trees (CART),
- are capable of addressing
- non-linear problems posed by PM2.5.

## Weka: Information

- http://www.cs.waikato.ac.nz/ml/weka/

## Input File Format

- Weka uses its own
- file format called:
-  	*.aarf
- All you need to do
- though is provide a
- *.csv file with variable
- names in the first line
- and Weka will convert

## aarf Format

- aarf format is simple anyway:
- ASCII file
- List of variable and type

- Then data follows,
-      comma separated
- Missing data marked as “?”

## Data Editing

- Data can be easily edited
- within Weka itself

## Analyzing Data

- Variables can be easily
- scanned with basic
- statistics and histograms
- provided by Weka

## Quick Analysis Tools

## Sampling and Test Data Set Options

## Functions Available

- WEKA includes a number of
- different techniques that can be useful for forecast development.
- These include:
- Linear and logistic regression
- Perceptron models
-    (Neural networks)

## Linear Regression

- Unfortunately, the “work horse” linear
- regression module in Weka is limited in
- usefulness:
- 	-No automatic stepwise function
- 	-Poor diagnostics
- Compare:  SYSTAT, Minitab

## Classification and Regression Trees (CART)

- A variety of classification
- algorithms are available.
- Standard algorithm is
- J48, which is a souped
- up version of the last
- free version of CART
- (Version 4.5)
- Commercial version is
- currently 5.0.

## CART Options

- A number of options
- are available to
- fine tune the CART
- Analysis:
- Minimum # of cases
-     per node
- Types of pruning: e.g.,
-      sub-tree raising
- Confidence values for
-     splitting nodes

## CART Diagnostics

- CART is notorious for using
- CPU resources but the WEKA
- version runs efficiently on my
- standard PC.
- Diagnostics are better for
- CART than linear regression.
- Example on left is of a 4 category
- PM2.5 CART forecast.

## CART Visualization

## Artificial Neural Networks (ANN)

- “Linear Regression by
-      a mob”
- Produces forecast by
- taking the weighted
- sum of predictors and
- then layering the
- process.

## Artificial Neural Networks - Summary

- Known samples (historical data) are used to “train” the network.
- Input data (xi) are assigned weights (wi) and combined in the “hidden” layer – like a set of linear
- regressions.  These sets are then combined in
- additional layers – like regressions of regressions.
- The sum of data and weights are transformed
- (“squashed”) to the range of the training data and error is measured.
- A supervised training algorithm uses output error to adjust network weights to minimize errors.

## Artificial Neural Networks – Pros/Cons

- Pro:  ANN’s are a powerful technique utilized across scientific disciplines.
- Pro:  Theoretically well suited to non-linear processes like air quality.
- Con:  Not transparent to users.  Hard to integrate into forecast thinking.
- Con:  Technically difficult to understand, raises risk of misuse.

## Example:  Neural Network Structure

- www.doc.ic.ac.uk/~sgc/teaching/v231/

## WEKA Neural Networks

- WEKA provides user control
- of training parameters:
-  # of iterations or epochs
- 	(“training time”)
-  Increment of weight adjustments
-  in back propogation (“learning
-  rate”)
-  Controls on varying changes
-  to increments (“momentum”)

## Conclusions

- Weka is a low-cost forecasting tool that has the potential to be a useful for air quality forecasting – particularly in situations where non-linear effects dominate.
- Some Weka modules are not fully developed for forecast algorithm development.
- Patience, use of textbook and Weka listserv are required to get the most out of the program.

## URLs of Interest

- Weka:
- _[http://www.cs.waikato.ac.nz/ml/weka ](http://www.cs.waikato.ac.nz/ml/weka)_
- Mailing List:
- _[https://list.scms.waikato.ac.nz/mailman/listinfo/wekalist](https://list.scms.waikato.ac.nz/mailman/listinfo/wekalist)_
- Mailing List Archives
- _[https://](https://list.scms.waikato.ac.nz/mailman/htdig/wekalist/)__[list.scms.waikato.ac.nz/mailman/htdig/wekalist](https://list.scms.waikato.ac.nz/mailman/htdig/wekalist/)__[/](https://list.scms.waikato.ac.nz/mailman/htdig/wekalist/)_
- Informal FAQ:
- _[http://www.public.asu.edu/~sksinghi/weka-faq.html](http://www.public.asu.edu/~sksinghi/weka-faq.html)_

## Acknowledgements

- The Delaware Valley Regional Planning Commission (DVRPC) – Mike Boyer and Sean Greene – and the member states (PA, DE and NJ) for supporting air quality forecast development.
- Dr. George Young of Penn State for his advice, patience and teaching skill.