Presentation by Daan Buekenhout (KU Leuven, Belgium) at the Symposium on Emulating 2D flood modelling, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 27 September 2023, Delft.

1. Surrogate model for real-
time urban pluvial flood
inundation mapping
Daan Buekenhout
Hydraulics & Geotechnics Section
Symposium on Emulating 2D flood modelling - Deltares, 27/09/2023

2. • Application
• Real-time model
• Rainfall nowcasting
• Flood mapping
• End-user interaction
Daan Buekenhout, Hydraulics and Geotechnics Section
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This talk

3. Project context
Digital Twins & Serious Games
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4. • Resilience of critical infrastructure
• 40 partners, 4 living labs
• Living lab Antwerp
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Partners

5. Digital Twin Serious Game
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Tools

6. • Short-term rainfall forecasts
• Real-time pluvial flood maps
• Integration within Digital Twin
• Software interaction/licenses
• Limited resources
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Initial model requirements
Project
Technical limitations

7. Stakeholder interviews
Design sprint
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8. Warning
Meteorological institute: rainfall thresholds (provincial level)
~ pre-alert
Actions
Ad hoc, no fixed script
• ‘buienradar’
• Establishing communication
• Waiting for phone calls
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Current situation
48h
24h
12h

9. • Flood maps: inundation depths & speeds
• Lead time: > 1 day
• Spatial resolution: sub-street level
• Accuracy: ~ cm
• Scale: region-wide
... not realistic (in short project horizon)
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Moonshot Thinking
Efficient resource allocation
& faster response times

10. • Flood maps: inundation depths
• Lead time: nowcasting (< 2 hours)
• Spatial resolution: street level
• Accuracy unit: ~ cm
• Scale: city centre (case study)
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Updated model requirements
Efficient resource allocation
& faster response times

11. Prior research
@KU Leuven
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12. Short Term Ensemble Prediction System STEPS-BE vs. pySTEPS
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Rainfall nowcast
20 members
Member 1
Member 20
Veithen, F., & Willems, P. (2023). Extreme rainfall data analysis for urban
flood forecasting. KU Leuven. Faculteit Ingenieurswetenschappen.

13. Semi-distributed (0D/1D) Fully-distributed (2D/1D)
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Hydrodynamic models
T5 T100
T20
> 1h

14. Hydrodynamic models

15. Conceptual sewer model + 𝑉𝑓𝑙𝑜𝑜𝑑 → flood depths
Bermúdez, M., Ntegeka, V., Wolfs, V., & Willems, P. (2018). Development and Comparison of Two Fast Surrogate Models for Urban Pluvial Flood Simulations.
Water Resources Management, 32(8), 2801–2815.
Conceptual sewer model + Probabilistic Yes/No
Li, X., & Willems, P. (2020). A hybrid model for fast and probabilistic urban pluvial flood prediction. Water Resources Research, 56(6).
Li, X., & Willems, P. (2020). Probabilistic flood prediction for urban sub-catchments using sewer models combined with logistic regression models.
In Urban Water Journal (Vol. 16, Issue 10, pp. 687–697). Taylor & Francis.
Artificial Neural Networks
González-Iñiguez, A., Muñoz P., Willems P. (2021). Influence of rainfall and boundary conditions complexity in the prediction power of surrogate urban
flood models based on neural networks. 15th International Conference on Urban Drainage, Melbourne, October, 2021.
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Surrogate flood mapping

16. Real-time surrogate
Combining PCA and LSTM
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...

17. Simplified boundary conditions
• Uniform rainfall
• High tide Scheldt
Limited to city center
→ 1 397 581 cells
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Flood map library

18. 12h event duration
5 min timestep (cf. radar)
80 composite storms (T1-100, current & future climate)
12 historical storms (> T3)
→ train-val-test: 64-12-16
→ 92 x 145 = 13 248 flood maps
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Flood map library
0
50
100
150
mm/h
T100 composite
0
50
100
150
200

19. 20 x 20 m aggregation ~ street level
→ 286 x 295 = 84 270 pixels
within mesh
→ 35 740 pixels
> 5 cm flood
→ 14 889 pixels
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Dimensionality reduction

20. Principal component analysis
Stacked Autoencoder (cf. Kao et al. 2021)
→ lower performance (e.g. 175 → 38 resulted in 0.0180 and 0.0274 m RMSE)
→ linear behaviour of this case study
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Dimensionality reduction
n cumulative variance RMSE train [m] RMSE test [m]
1203 0.9999 0.0007 0.0074
175 0.999 0.0023 0.0084
24 0.99 0.0074 0.0130
4 0.9 0.0251 0.0311

21. Long Short-Term Memory
→ Recurrent NN
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Temporal evolution
n (hidden state) epochs RMSE* train [m] RMSE* val [m] RMSE* test [m]
5 34 0.0732 0.1058 0.1020
10 75 0.0256 0.0368 0.0357
15 162 0.0189 0.0285 0.0264
20 138 0.0183 0.0276 0.0261
25 130 0.0144 0.0243 0.0214
*including reconstruction error
0
0.5
1
1.5
2
2.5
3
3.5
1 21 41 61 81 101 121
RMSE
[-]
epochs
Train Test
Training (Adam, MSE)
• batch size = 16
• early stopping (n=5)
• lr = 1e-4
• decay = 1e-8

22. End user evaluation
Historical event
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23. • 301 interventions
• €2 242 000
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4th of July 2021
mm

24. Visual performance
Interventions
Flood depths at peak
Surrogate
• Uniform rainfall
• Fixed tide

25. Visual performance
Interventions
Flood depths at peak
Reference (ICM)
• Uniform rainfall
• Fixed tide

26. Visual performance
Interventions
Flood depths at peak
‘truth’ (ICM)
• Spatial rainfall
• Actual tide

27. End user evaluation
~ flood expert evaluation?
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28. Daan Buekenhout, Hydraulics and Geotechnics Section
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Flood mapping > rainfall forecast?
0
10
20
30
40
50
60
70
Yes No They have equal value
%

29. Daan Buekenhout, Hydraulics and Geotechnics Section
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Regression vs. classification
0
10
20
30
40
50
60
70
Yes, I also need the rough estimation in cm No, the colour classification is sufficient
%

30. Daan Buekenhout, Hydraulics and Geotechnics Section
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Spatial resolution (20x20m) sufficient?
0
10
20
30
40
50
60
70
Yes No, more detail required No, it is too detailed
%

31. Daan Buekenhout, Hydraulics and Geotechnics Section
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Time gain
0
5
10
15
20
25
30
35
less than 5 minutes 5 - 9 minutes 10 - 14 minutes more than 15 minutes can't estimate
%
• No preventive actions
• Planning
• Establishing communication
• Resource allocation

32. Lessons learned
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33. Value of pluvial flood mapping potential is clear
End users want:
• real-time
• alerts
• temporal evolution
• region-wide
• accuracy
• Scenarios, mitigation action lists, ...
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Application

34. Gap between flood modellers and end users
• Goals (e.g. high performance vs. interpretability)
• Concepts (e.g. probabilities)
• Responsibilities (e.g. alerts)
Need for multidisciplinary teams
• UX/UI → integration within existing systems
• Communicators/educators
• CI experts
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End user interaction

35. Rainfall forecasts are critical
Presented flood mapping = work in progress
• Extended flood map library (tidal effect, heterogeneous rainfall, ...)
• Uncertainty analysis (radar observation, forecast, flood mapping)
• Focus on flood extremes
→ PhD research
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Model chain

36. daan.buekenhout@kuleuven.be
patrick.willems@kuleuven.be
Surrogate model for real-time urban pluvial
flood inundation mapping