1. A Fuzzy Case-Based Weather Prediction for Airbases
Bulent KISKAC
Harun YARDIMCI

2. Outline Problem & Domain Description Knowledge Acquisition Methods
Knowledge Representation
Type of Task
Problem Characteristic
System Features
Solution Features
Tool Selection Criteria
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3. Problem/Domain Description
Flight planning and air traffic control are tightly related with the weather parameters.
When ceiling and horizontal visibility at an airbase are low, in order to maximize the flight safety, some of task are postponed.
Correctly forecast timing of a ceiling and visibility event could be expected to result in a savings from great costs of flight delays and planning.
Short-range prediction of ceiling and visibility are very important attributes of weather condition.
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4. Knowledge Acquisition Methods
Past airport weather observations,
(1 years of hourly observations).
Recent and current observations,
(for testing purpose).
Domain Expert guidance,
(identifying the attributes to be used and
describing degrees of similarity between such attributes like very near, near, slightly near).
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5. Knowledge Representation
Category
temporal
cloud ceiling and visibility
wind
precipitation
spread and temperature
pressure
Attribute
date
hour
cloud amount(s) cloud ceiling height visibility
wind direction wind speed
precipitation type precipitation intensity
dew point temperature dry bulb temperature
pressure trend
Units
Julian date of year (wraps around)
hours offset from sunrise/sunset
tenths of cloud cover (for each layer) height in metres of ³ 6/10ths cloud cover horizontal visibility in metres
degrees from true north knots
nil, rain, snow, etc. nil, light, moderate, heavy
degrees Celsius degrees Celsius
kiloPascal × hour -1
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6. Knowledge Representation (Cont.)
All of these attributes are continuous.
Precipitation is nominal (e.g., rain, snow, etc.).
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7. Knowledge Representation (Cont.)
Sample consecutive hourly observation…
YY/MM/DD/HH Ceiling Vis Wind Wind Dry Dew MSL Station Cloud
Directn Speed Bulb Point Press Press Amount
30's m km 10's deg km/hr deg C deg C kPa kPa tenths Weather
64/ 1/ 2/
64/ 1/ 2/ ZR-
64/ 1/ 2/ ZR-F
64/ 1/ 2/ ZR-F
64/ 1/ 2/ R-F
64/ 1/ 2/ R-F
64/ 1/ 2/ R-F
64/ 1/ 2/ F
64/ 1/ 2/ F
64/ 1/ 2/ R-F
64/ 1/ 2/ F
64/ 1/ 2/ F
64/ 1/ 2/ F
64/ 1/ 2/
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8. Problem Characteristic
Knowledge about meteorological and temporal features that experienced forecasters use to construct analogous climatological scenarios.
We have to use an expert system to produce highly accurate forecast because the weather prediction expert system manipulate the airports' historical databases.
We hope to produce highly accurate short term weather forecasts in a few seconds within tolerance of 100 ft ceiling and 400 m visibility
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9. National Weather Service
System Features
Aircraft Meteorological& State Data
Aircraft/Ground Data Link
EXPERT SYSTEM
Aircraft/Ground Data Link
Flight Deck Display
National Weather Service
Airport Weather Sensor Suite
Historical Airbase Database
Flight Control Tower
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10. Solution Features Fuzzy Logic Case-Based Reasoning
K Nearest Neighbor Algorithm
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11. Fuzzy Logic
Use of fuzzy logic has increased exponentially over the past 30 years,based on the number of uses of the word “fuzzy” in titles of articles in engineering and mathematical journals.
In meteorological systems,use of fuzzy logic began about ten years ago.
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12. Solution Features Fuzzy Set
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13. What is Case-Based Reasoning?
A kind of ‘table look-up’
CBR system looks up in case base (indexed store of cases) past cases that bear on current problem
Indexing and case representation facilitate retrieving relevant cases and comparing them with current problem
CBR system applies information in retrieved cases to analyzing or solving problem
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14. When should CBR be used?
A large volume of historical data already exists
Experts talk about their domain by giving examples
Experience is as valuable as textbook knowledge
Problems are not fully understood (weak models, little domain knowledge available)
There are a lot of exceptions to rules
There is a need to build a corporate memory and transfer expertise among personnel
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15. Problem & Solution Spaces
Retrieve
Adapted Solution
Problem Space
Adapt
Problem Specification
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16. potential endless loop
Classic CBR Flowchart
CBR needs methods for acquiring domain knowledge for retrieval and adaptation.
difficult problem
potential endless loop
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17. Case-Based Reasoning Source
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18. k-Nearest Neighbor(s) Technique
The closer the neighbors, the more useful they are for prediction. It is reasonable to assume that observations which are close together (according to some appropriate metric) will have the same classification.
It may be reasonable to weight the evidence of a neighbor close to an unclassified observation more heavily than the weight of another neighbor which is at a greater distance from the unclassified observation.
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19. k-Nearest Neighbor(s) Algorithm
Collect Most Similar Analogs, to make prediction
Distances” determined by fuzzy similarity-measuring functions, expertly tuned, all applied together simultaneously.
k-nearest algorithm demo…
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20. Tool Selection Criteria
A database MS SQL Server2000 for storing the case data
Java Programming Language with JBuilder9 IDE
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21. Future Work
Graphic user interface: Let expert forecasters guide the data-mining to test “what-if” weather scenarios based on various possible conditions.
Alert Module: A smart alert, and thus help forecasters to increase their situational awareness.
More predictors: Allow data-mining to be better conditioned, e.g., duration of precipitation, sun factors (length of day, strength of sun), wind (back trajectory, wind run, source region, etc.
Data fusion: Exploit available predictive data and employ other data fusion techniques such as Neural Net,Rule Based systems
Integration: Automated input receiving from Weather Sensor .
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22. Conclusion
By building expert systems that combine forecaster expertise, AI, large amounts of data (climatological and current), and currently available computing power, we can increase forecast quality and increase forecasting efficiency.
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23. Questions?
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