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A Tool for Visualising the output of a
DBN for fog forecasting
Information Technology
Tali Boneh, Xuhui Zhang,
Ann Nichols...
Overview
• The problem: fog forecasting (at airports)
• Previous BN modelling:
– “static” Bayesian decision network (no ex...
Fog Formation
Fog Forecasting for Airlines
• Terminal Aerodrome Forecast (TAF) issued every 6
hours; valid for 30h
3pm -----------------...
ARC Linkage
2002-2004
2005 2006- 2008 2010200920042003 2011 20152012 2013
Boneh’s PhD
2003-2008
ARC Linkage
2012-2014 (201...
Phase 1: 2004 – 2008
(static Bayesian Decision Network)
Four priority airports were chosen for fog forecast improvement:
•...
“Static” semi-causal BN for fog forecasting
Decision
saynofog
CodeGreyLessThan5
CodeGrey5
CodeGrey10
CodeGrey20
sayfog
28....
BOFFIN: deployed 2006
• Decision support tool in operational use for fog
forecasting at Melbourne airport
– BN as back-end...
Dynamic Bayesian networks
• For explicit reasoning over time, a copy of each process
variable X at each time T: X0, X1, … ...
Weather
Fog
Weather
Fog
Weather
Fog
Clearance
Onset
times
Clearance
Onset
times
Weather Weather
Forecast time
Now
Forecast...
Weather
Fog
Weather
Fog
Weather
Fog
Clearance
Onset
times
Clearance
Onset
times
Weather Weather
Forecast time
Now
Forecast...
12
Melbourne Fog DBN: Prototype 1
14
5 Weather variables (discretised): children of
both “length of night” and the Fog node
15
Melbourne Fog DBN: Prototype 1
16
Change nodes:
If Fog, Clearance? Time of clearance?
If NoFog, Onset? Time of Onset?
If NoFog, then Onset, will it clear?
Melbourne Fog DBN: Prototype 1
17
Visualisation and understandability: 8 time slices
(Netica demo)
ForecastTime Info.
Nodes midN 3am 6am
F_F_TmidN ~{fog} nofog1 nofog1
delta_F1_TmidN * onset onset
delta_F2_TmidN * nochang...
First attempt at presenting DBN outputs
A fog case (fog 00:15 - 4:00)
probabilities of ‘fog now’ at different forecast tim...
Fog DBN Visualisation Tool
• Developed using D3, a JavaScript library for
manipulating documents based on data within a we...
Selected Cases for Demo
22
# Actual DBN date onset clearance Comment and
forecast at 3pm
1 fog fog
hit
28/06/2011 19:00 2:...
X-axis: time over night
Y-axis: P(X=x|Observations to date)
30% chance of fog
Prior Select
X=x
To
display
Case “fog hit” (...
fogTonight: hovering over point brings up probability
Note: Fog prior not selected but is always shown
“faded”
Displaying more info: e.g. probability of fogOnset
Displaying more info: e.g. probability of fogOnset
Overlay showing not only probability for fogOnset in next 3
hrs, but distribution of times for onset and clearance
Overlay showing not only probability for fogOnset in next 3
hrs, but distribution of times for onset and clearance
Probability of “fogNow”, option for overlay with
distribution for time of fog clearance
Demo
No fog case, correctly predicted
No fog case, false alarm
Showing probabilities for single value, at different
forecast times
No fog case, false alarm
No fog case, false alarm
Where are we at?
• Bur. of Met collaborators ‘like’ the DBN visualisation tool
– Understanding of the DBN model
– Assistin...
A Tool for Visualising the output of a  DBN for fog forecasting
A Tool for Visualising the output of a  DBN for fog forecasting
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A Tool for Visualising the output of a  DBN for fog forecasting Slide 1 A Tool for Visualising the output of a  DBN for fog forecasting Slide 2 A Tool for Visualising the output of a  DBN for fog forecasting Slide 3 A Tool for Visualising the output of a  DBN for fog forecasting Slide 4 A Tool for Visualising the output of a  DBN for fog forecasting Slide 5 A Tool for Visualising the output of a  DBN for fog forecasting Slide 6 A Tool for Visualising the output of a  DBN for fog forecasting Slide 7 A Tool for Visualising the output of a  DBN for fog forecasting Slide 8 A Tool for Visualising the output of a  DBN for fog forecasting Slide 9 A Tool for Visualising the output of a  DBN for fog forecasting Slide 10 A Tool for Visualising the output of a  DBN for fog forecasting Slide 11 A Tool for Visualising the output of a  DBN for fog forecasting Slide 12 A Tool for Visualising the output of a  DBN for fog forecasting Slide 13 A Tool for Visualising the output of a  DBN for fog forecasting Slide 14 A Tool for Visualising the output of a  DBN for fog forecasting Slide 15 A Tool for Visualising the output of a  DBN for fog forecasting Slide 16 A Tool for Visualising the output of a  DBN for fog forecasting Slide 17 A Tool for Visualising the output of a  DBN for fog forecasting Slide 18 A Tool for Visualising the output of a  DBN for fog forecasting Slide 19 A Tool for Visualising the output of a  DBN for fog forecasting Slide 20 A Tool for Visualising the output of a  DBN for fog forecasting Slide 21 A Tool for Visualising the output of a  DBN for fog forecasting Slide 22 A Tool for Visualising the output of a  DBN for fog forecasting Slide 23 A Tool for Visualising the output of a  DBN for fog forecasting Slide 24 A Tool for Visualising the output of a  DBN for fog forecasting Slide 25 A Tool for Visualising the output of a  DBN for fog forecasting Slide 26 A Tool for Visualising the output of a  DBN for fog forecasting Slide 27 A Tool for Visualising the output of a  DBN for fog forecasting Slide 28 A Tool for Visualising the output of a  DBN for fog forecasting Slide 29 A Tool for Visualising the output of a  DBN for fog forecasting Slide 30 A Tool for Visualising the output of a  DBN for fog forecasting Slide 31 A Tool for Visualising the output of a  DBN for fog forecasting Slide 32 A Tool for Visualising the output of a  DBN for fog forecasting Slide 33 A Tool for Visualising the output of a  DBN for fog forecasting Slide 34 A Tool for Visualising the output of a  DBN for fog forecasting Slide 35 A Tool for Visualising the output of a  DBN for fog forecasting Slide 36
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A Tool for Visualising the output of a DBN for fog forecasting

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Fog events occur at Melbourne Airport, Aus-
tralia, approximately 12 times each year. Un-
forecast events are costly to the aviation industry,
cause disruption and are a safety risk. Thus, there
is a need to improve operational fog forecasting.
However, fog events are difficult to forecast due
to the complexity of the physical processes and
the impact of local geography and weather ele-
ments.
Bayesian networks (BNs) are a probabilistic rea-
soning tool widely used for prediction, diagno-
sis and risk assessment in a range of application
domains. Several BNs for probabilistic weather
prediction have been previously reported, but to
date none have included an explicit forecast de-
cision component and none have been used for
operational weather forecasting. A Bayesian De-
cision Network (Bayesian Objective Fog Fore-
cast Information Network; BOFFIN) has been
developed for fog forecasting at Melbourne Air-
port based on 34 years of data (1972-2005). Pa-
rameters were calibrated to ensure that the net-
work had equivalent or better performance to
prior operational forecast methods, which lead
to its adoption as an operational decision sup-
port tool. The operational use of the network
by forecasters over an 8 year period (2006-2013)
has been evaluated [1], showing significantly im-
proved forecasting accuracy by the forecasters
using the network, as compared with previous
years. BOFFIN-Melbourne has been accepted
by forecasters due to its skill, visualisation and
explanation facilities, and because it offers fore-
casters control over inputs where a predictor is
considered unreliable...

Paper: http://abnms.org/conferences/abnms2015/papers/ABNMS_2015_paper_16%20-%20A%20Tool%20for%20Visualising%20the%20output%20of%20a%20DBN%20for%20fog%20forecasting.pdf

Tali Boneh, Xuhui Zhang, Ann Nicholson and Kevin Korb


Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/

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A Tool for Visualising the output of a DBN for fog forecasting

  1. 1. A Tool for Visualising the output of a DBN for fog forecasting Information Technology Tali Boneh, Xuhui Zhang, Ann Nicholson & Kevin Korb Faculty of Information Technology Monash University
  2. 2. Overview • The problem: fog forecasting (at airports) • Previous BN modelling: – “static” Bayesian decision network (no explicit time) – Operational decision support tool since 2006 • DBN for fog forecasting • Fog DBN Visualisation tool
  3. 3. Fog Formation
  4. 4. Fog Forecasting for Airlines • Terminal Aerodrome Forecast (TAF) issued every 6 hours; valid for 30h 3pm ---------------------------------| ~ 9am (Perth) /11am (Melbourne) 9pm ------------------------| ~ 9am 3am ---------------| ~ 9am • When fog probability ≥ 30%, fog included in the TAF • If fog forecasted, aircrafts must carry enough fuel to – reach an alternative airport – maintain a holding pattern above the airport • When chance of fog >5% but <30%, forecast is “Code Grey”
  5. 5. ARC Linkage 2002-2004 2005 2006- 2008 2010200920042003 2011 20152012 2013 Boneh’s PhD 2003-2008 ARC Linkage 2012-2014 (2015) Boneh post-doc Boneh @ BoM 2002 2014 1. ARC Linkage 2012-2014. Improving Meteorological Forecasting with Knowledge Management Systems. 2. ARC Linkage 2012-2014.Temporal and spatial Bayesian network modelling for improved fog forecasting ARC $224K, BoM $148K Boneh , T. (2010) Ontology and Bayesian Decision Networks for Supporting the Meteorological Forecasting Process. PhD thesis, Monash and Australian Bureau of Meteorology collaboration 2003-2015 BOFFIN decision support tool for forecasting fog at Melbourne airport
  6. 6. Phase 1: 2004 – 2008 (static Bayesian Decision Network) Four priority airports were chosen for fog forecast improvement: • Melbourne (~ 12-13 fogs) – Networks for 3pm, 6/9pm, midnight • Sydney (~ of 4 to 5 fogs per year, largest traffic volumes), – Networks for midnight, 2am and 3am • Canberra (~ 42 fogs) – Networks for 6pm, midnight and 3am • Perth (~ 12 fogs per year, large distances to the nearest alternate airports – A network for 3pm Prediction: Fog Yes or No? No prediction of onset and clearance Month January February March April May June July August September October November December 8.46 7.71 8.46 8.18 8.46 8.18 8.70 8.57 8.18 8.46 8.18 8.46 LengthOfNight Nov to Jan Feb and Oct March and Sept Apr and Aug May to July 25.1 16.2 16.6 16.8 25.3 RainNoRain 0 to 4.5 >= 4.5 90.6 9.41 Fog fog nofog 3.30 96.7 U LapseRate9pmCont < 2.05 2.05 to 2.75 2.75 to 3.25 >= 3.25 26.2 18.1 17.7 37.9 2.74 ± 0.75 Moisture Vfav fav unfav 26.4 19.2 54.4 SternParkyn 0 to 1 1 to 2 2 to 5 5 to 10 10 to 15 15 to 30 30 to 100 46.8 15.1 17.5 9.79 4.12 4.40 2.28 4.8 ± 11 Decision saynofog CodeGreyLessThan5 CodeGrey5 CodeGrey10 CodeGrey20 sayfog 28.7044 28.9378 28.0293 26.3915 24.6700 22.6587 Gradient Vfav fav unfav 31.2 19.3 49.4
  7. 7. “Static” semi-causal BN for fog forecasting Decision saynofog CodeGreyLessThan5 CodeGrey5 CodeGrey10 CodeGrey20 sayfog 28.7044 28.9378 28.0293 26.3915 24.6700 22.6587 Month January February March April May June July August September October November December 8.46 7.71 8.46 8.18 8.46 8.18 8.70 8.57 8.18 8.46 8.18 8.46 LengthOfNight Nov to Jan Feb and Oct March and Sept Apr and Aug May to July 25.1 16.2 16.6 16.8 25.3 RainNoRain 0 to 4.5 >= 4.5 90.6 9.41 Fog fog nofog 3.30 96.7 U LapseRate9pmCont < 2.05 2.05 to 2.75 2.75 to 3.25 >= 3.25 26.2 18.1 17.7 37.9 2.74 ± 0.75 Moisture Vfav fav unfav 26.4 19.2 54.4 SternParkyn 0 to 1 1 to 2 2 to 5 5 to 10 10 to 15 15 to 30 30 to 100 46.8 15.1 17.5 9.79 4.12 4.40 2.28 4.8 ± 11 Gradient Vfav fav unfav 31.2 19.3 49.4 Background Decision Guidance Predictors Target
  8. 8. BOFFIN: deployed 2006 • Decision support tool in operational use for fog forecasting at Melbourne airport – BN as back-end reasoning engine – Integrated with data stream of current weather observations – Allows forecasters to adjust data entered as evidence (“what-if” functionality) • Improved forecasting 2006-2014 (fewer missed fogs without increase in false alarms) Boneh et al. (To Appear). Weather and Forecasting. Fog Forecasting for Melbourne Airport using a Bayesian Decision Network. • Limitation: no temporal aspect to prediction, i.e. time of onset and clearance
  9. 9. Dynamic Bayesian networks • For explicit reasoning over time, a copy of each process variable X at each time T: X0, X1, … XT-1, XT, XT+1, … • Typically: – Same structure within each time slice (intra-slice arcs) – Stationary time series (i.e. parameters not time dependent), so summarised in two time slice DBN (with sliding window), used with “roll-up, roll-out” approximation
  10. 10. Weather Fog Weather Fog Weather Fog Clearance Onset times Clearance Onset times Weather Weather Forecast time Now Forecast time Future Forecast time Future NWP Predictions Current Obs NWP Predictions Current Obs NWP Predictions DBN Framework for Fog
  11. 11. Weather Fog Weather Fog Weather Fog Clearance Onset times Clearance Onset times Weather Weather Forecast time Now Forecast time Future Forecast time Future NWP Predictions Current Obs NWP Predictions Current Obs NWP Predictions DBN Prototype 1Add observations directly Non-causal (NB)
  12. 12. 12 Melbourne Fog DBN: Prototype 1
  13. 13. 14 5 Weather variables (discretised): children of both “length of night” and the Fog node
  14. 14. 15 Melbourne Fog DBN: Prototype 1
  15. 15. 16 Change nodes: If Fog, Clearance? Time of clearance? If NoFog, Onset? Time of Onset? If NoFog, then Onset, will it clear?
  16. 16. Melbourne Fog DBN: Prototype 1 17 Visualisation and understandability: 8 time slices (Netica demo)
  17. 17. ForecastTime Info. Nodes midN 3am 6am F_F_TmidN ~{fog} nofog1 nofog1 delta_F1_TmidN * onset onset delta_F2_TmidN * nochange nochange Onset_TmidN * 0.25 0.25 Clear_if_Clearance_TmidN * * * Clear_if_Onset_TmidN * * * Wvis_TmidN 1 1 1 F_F_T3am * fog fog delta_F1_T3am * * clearance delta_F2_T3am * * * Onset_T3am * * * Clear_if_Clearance_T3am * * 1 Clear_if_Onset_T3am * * * Wvis_T3am * 1 1 F_F_T6am * * ~{fog} delta_F1_T6am * * * delta_F2_T6am * * * Onset_T6am * * * Clear_if_Clearance_T6am * * * Clear_if_Onset_T6am * * * Wvis_T6am * * 4 First attempt at presenting DBN outputs A fog case (fog 00:15 - 4:00) midN 3am 6am net slice
  18. 18. First attempt at presenting DBN outputs A fog case (fog 00:15 - 4:00) probabilities of ‘fog now’ at different forecast times forecast time 9pm 0.00100943 0.0013859 0.00210729 0.00293861 0.00580142 0.0107448 0.0203179 0.0339942 0 0.00858423 0.0214569 0.0355907 0.0516443 Probabilities Start interval Time forecast time 3am 0.00100943 0.0013859 0.00210729 0.00293861 0.00580142 0.0107448 0.0203179 0.0339942 1 0.977578 0.903714 Probabilities Start interval Time forecast time midN 0.00100943 0.0013859 0.00210729 0.00293861 0.00580142 0.0107448 0.0203179 0.0339942 0 0.0393763 0.0479343 0.0538041 Probabilities Start interval Time 0.00100943 0.0013859 0.00210729 0.00293861 0.00580142 0.0107448 0.0203179 0.0339942 0 0.0799313 Probabilities Start interval Time Prior 6am Forecast Time forecast time 6am forecast time 6pm 0.00100943 0.0013859 0.00210729 0.00293861 0.00580142 0.0107448 0.0203179 0.0339942 0 0.000694723 0.00468568 0.0129131 0.0275323 0.0488081 Probabilities Start interval Time • Our forecasters had no idea what the DBN was doing/saying • And we weren’t sure either!
  19. 19. Fog DBN Visualisation Tool • Developed using D3, a JavaScript library for manipulating documents based on data within a web browser, uses a combination of HTML, SVG, and CSS • Extends Matthew Weber’s “block” template • Developed in collaboration with visualisation research group at Monash
  20. 20. Selected Cases for Demo 22 # Actual DBN date onset clearance Comment and forecast at 3pm 1 fog fog hit 28/06/2011 19:00 2:30 Always looked like fog (TAF) 2 no fog no fog correct 4/07/2003 Always looked like no fog (TAF) 3 fog no fog miss 3/07/2008 7:21 8:00 Looked like no fog until 6am (CG) 4 no fog fog false alarm 21/05/2009 Fog on previous day cleared at 9:47am (TAF)
  21. 21. X-axis: time over night Y-axis: P(X=x|Observations to date) 30% chance of fog Prior Select X=x To display Case “fog hit” (fog event successfully forecast): sequence of observations for weather variables and fog, from midday to following 9am Forecast time – here midday Visualisation of forecast time
  22. 22. fogTonight: hovering over point brings up probability Note: Fog prior not selected but is always shown “faded”
  23. 23. Displaying more info: e.g. probability of fogOnset
  24. 24. Displaying more info: e.g. probability of fogOnset
  25. 25. Overlay showing not only probability for fogOnset in next 3 hrs, but distribution of times for onset and clearance
  26. 26. Overlay showing not only probability for fogOnset in next 3 hrs, but distribution of times for onset and clearance
  27. 27. Probability of “fogNow”, option for overlay with distribution for time of fog clearance
  28. 28. Demo
  29. 29. No fog case, correctly predicted
  30. 30. No fog case, false alarm
  31. 31. Showing probabilities for single value, at different forecast times
  32. 32. No fog case, false alarm
  33. 33. No fog case, false alarm
  34. 34. Where are we at? • Bur. of Met collaborators ‘like’ the DBN visualisation tool – Understanding of the DBN model – Assisting with ongoing development • No usability testing as such • Currently doing lots of experimental work on DBN – Selection of weather variables as predictors – Discretisation of weather variables, onset and clearance times • Specific research project outcomes: – prototype DBN, papers • Future: embed DBN in support tool for operational use (1-2 years?)

Fog events occur at Melbourne Airport, Aus- tralia, approximately 12 times each year. Un- forecast events are costly to the aviation industry, cause disruption and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult to forecast due to the complexity of the physical processes and the impact of local geography and weather ele- ments. Bayesian networks (BNs) are a probabilistic rea- soning tool widely used for prediction, diagno- sis and risk assessment in a range of application domains. Several BNs for probabilistic weather prediction have been previously reported, but to date none have included an explicit forecast de- cision component and none have been used for operational weather forecasting. A Bayesian De- cision Network (Bayesian Objective Fog Fore- cast Information Network; BOFFIN) has been developed for fog forecasting at Melbourne Air- port based on 34 years of data (1972-2005). Pa- rameters were calibrated to ensure that the net- work had equivalent or better performance to prior operational forecast methods, which lead to its adoption as an operational decision sup- port tool. The operational use of the network by forecasters over an 8 year period (2006-2013) has been evaluated [1], showing significantly im- proved forecasting accuracy by the forecasters using the network, as compared with previous years. BOFFIN-Melbourne has been accepted by forecasters due to its skill, visualisation and explanation facilities, and because it offers fore- casters control over inputs where a predictor is considered unreliable... Paper: http://abnms.org/conferences/abnms2015/papers/ABNMS_2015_paper_16%20-%20A%20Tool%20for%20Visualising%20the%20output%20of%20a%20DBN%20for%20fog%20forecasting.pdf Tali Boneh, Xuhui Zhang, Ann Nicholson and Kevin Korb Seventh Annual Conference of the Australasian Bayesian Network Modelling Society conference (ABNMS2015) - Monash Caulfield, Melbourne, Australia 2015: http://abnms.org/conferences/abnms2015/

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