weka: a useful tool for air quality forecasting
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Weka: A Useful Tool for Air Quality Forecasting
William F. Ryan
Department of Meteorology
The Pennsylvania State University
2007 National Air Quality Conference, Orlando
Weka
The weka, or woodhen, is a birdnative to New Zealand. Weka is
also the name of a suite of machinelearning software tools, written in
Java, and developed at the Universityof Wiakato in New Zealand.
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-behavedstatistically. Distribution is nearnormal with a strong associationwith maximum temperature. As a
result, linear techniques areuseful.
PM2.5 (right panel) is not well-behaved. Distribution is skewed,
no strong association with anyparticular 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 ownfile format called: *.aarf
All you need to dothough is provide a*.csv file with variablenames in the first lineand Weka will convert
aarf Format
aarf format is simple anyway:
ASCII fileList of variable and type
Then data follows, comma separated
Missing data marked as “?”
Data Editing
Data can be easily editedwithin Weka itself
Analyzing Data
Variables can be easilyscanned with basic
statistics and histogramsprovided 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 regressionPerceptron models (Neural networks)
Linear Regression
Unfortunately, the “work horse” linearregression module in Weka is limited inusefulness:
-No automatic stepwise function-Poor diagnostics
Compare: SYSTAT, Minitab
Classification and Regression Trees (CART)
A variety of classificationalgorithms are available.
Standard algorithm isJ48, which is a souped up version of the lastfree version of CART(Version 4.5)
Commercial version iscurrently 5.0.
CART Options
A number of optionsare available tofine tune the CARTAnalysis:
-Minimum # of cases per node-Types of pruning: e.g., sub-tree raising-Confidence values for splitting nodes
CART Diagnostics
CART is notorious for usingCPU resources but the WEKAversion runs efficiently on mystandard PC.
Diagnostics are better forCART than linear regression.
Example on left is of a 4 categoryPM2.5 CART forecast.
CART Visualization
Artificial Neural Networks (ANN)
“Linear Regression by a mob”
Produces forecast bytaking the weightedsum of predictors andthen 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 linearregressions. 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 controlof 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
• Mailing List: – https://list.scms.waikato.ac.nz/mailman/listinfo/wekalist
• Mailing List Archives– https://list.scms.waikato.ac.nz/mailman/htdig/wekalist/
• Informal FAQ:– 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.