1) ProRail monitors ice formation on overhead power lines which can disrupt train services. They have observations of distortions in electric signals from 2011-2017 but need forecasts to plan mitigation. 2) The presentation proposes a data-driven approach combining ProRail observations with weather data to predict probability of ice formation. 3) A model was developed using Gaussian processes on exploratory data analysis of ProRail observations and weather variables to classify days as frost or normal. The model shows good results and captures real world weather patterns. Further work will develop a high resolution forecast and physics-based hazard model.

1. IEEE eScience Conference
Weather & Climate Science
in the Digital Era
Irene Garcia-Marti
Gerard van der Schrier
Jan Willem Noteboom
Paul Diks (ProRail)
Detecting probability of
ice formation on
overhead lines of the
Dutch railway network
31st October 2018

2. › Ice formation is a potential
source of infrastructure
failure:
– Overhead lines
– Aerial cables
– Power lines
– Communication towers
– Antennas
Motivation
30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 2
Source: Wikicommons

3. › Ice formation has the
potential of disrupting
railway services:
– Passenger services
– Commercial services
› Distrust in the service
› Economic loss
Motivation
30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 3
Source: ProRail

4. › ProRail:
– 7,000 km of railway and related
infrastructure (tunnels,
viaducts, crossings)
– Formation of ice causes
distortions in the electric signal
– Monitoring these distortions
since 2011 in regional centers
Motivation
30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 4
Source:ProRailSource: WikicommonsSource: ProRail

5. › Models of ice accretion:
– Laboratory conditions
– Empirical fitting
› Makkonen’s work:
𝑑𝑑𝑑𝑑
𝑑𝑑𝑑𝑑
= α1α2α3 𝑤𝑤𝑤𝑤𝑤𝑤
– α1: Collision efficiency
– α2: Sticking efficiency
– α3: Accretion efficiency
› 20+ complex geophysical
parameters:
– Examples: droplet diameter,
viscosity of air, heat of
vaporization, convective heat
transfer coefficient, long wave
radiation
› Unable to calculate them for
the national scale
Physical model
5
Source: Wikicommons

6. Will ice formation on overhead lines cause major train disruptions tomorrow?
ProRail
Needs forecast to plan
ice mitigation strategies
Propose
Data-driven approach combining
ProRail observations with
weather data
Envision
Approach contributes at
reducing the impact of
disruption over services

7. › ProRail data:
– Observations: 4,166
– Period: 2011-2017
› Observation:
– Metadata: time and date of the
distortion
– Data: closest train station,
values of distorted signal,
number of trains measuring the
distortion
Exploratory data
analysis
7

8. Exploratory data analysis
8

9. Workflow › Temperature
30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 9

10. › Variables extracted from the
KNMI network of automatic
weather stations
› Routine interpolation procedure
to obtain continuous surfaces:
– Daily gridded georeferenced
layers
– 1km resolution, 2011-2017 period
› Table with 4,166 rows and 4
columns
Feature engineering
30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 10

11. Modelling frost probability
› Binary classification: labelling data + data balancing
11
1: frost days (many distortions)
0: normal days (few distortions)

12. Modelling frost probability
› Binary classification: Gaussian processes + model selection
– GP:
 Generalization of a MV Gaussian distribution to N-dimensional space
 Finds a distribution over functions consistent with the observed data
 Begins with a prior distribution: defines the wiggliness of the space
 Composed by a mean function and covariance function (kernel)
– Model selection:
 RBF kernel with exploration of the parameter space
 Model is trained with different (but balanced) volumes of data
30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 12

13. Modelling frost probability
› Results
30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 13

14. Modelling frost
probability
30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 14

15. 30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 15

16. 30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 16

17. 30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 17

18. 30 oktober 2018
Koninklijk Nederlands Meteorologisch Instituut 18

19. Conclusions and further work
› Conclusions:
– Developed a model based on ground observations and weather data capable
of detecting probability of frost formation at the national scale
– Model presents good statistical metrics and the results make sense when
compared to real world weather warnings
› Further work:
– Gradually move to a high-resolution frost probability forecast
– Develop a hazard model based in physics for ice formation
19

20. Questions?
garciamarti@knmi.nl
Thanks for your
attention