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Real-time flood extent maps
based on social media
Arnejan van Loenen
Dirk Eilander
Patricia Trambauer
Jurjen Wagemaker (FloodTags.com)
Email: [email protected]
Pilot area: Jakarta
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Frequent floods in
Jakarta
Feb 9th-11th 2015
• > 728 000 tweets
• peak 900 tweets/minute
• 2200 incl. water depth
• ~ 900 tweets (40%)
location
• For comparison: 10-20
water level gauges (not
all functioning)
“15.31 #Flood in Cipinang
Melayu where the water
level reaches up to +/ - 2m”
Flood observations in the “Twitter cloud”
Data integration platform: Delft-FEWS
Global Flood Monitor
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Contents
• What social media can add to flood disaster management
• How to find the useful information
• Real-time flood mapping
• There is some uncertainty though
Information sources during floods
Telemetry CCTV
Rain radar Remote Sensing
Potential use of social media
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-Finding flood prone areas
-validating hydrodynamic
models
- Currently flooded areas
- People in need
- Evacuation routes
Flood forecasting
Flood prevention
Disaster management
Disaster recovery - Flood Impact analysis
How to find the useful information
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Is it a flood prone area?
Method 1: Height Above Nearest Drainage (HAND)
The HAND model normalizes topography according to the local
relative heights found along the drainage network
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Is it a flood prone area?
Method 2: Slope
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Is it a flood prone area?
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• HAND (<3.5m) & Slope (<0.2 m-1) match well with flood
areas from MODISlance archive
• Uncertainty in threshold derived from bootstrapping HAND
and Slope flood areas
• Could be used to improve geo-location tweets
What is heavy rain?
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90th percentile
99th percentile
Local hourly precipitation Accumulated daily precipitation
Contextual validation method
Static maps Dynamic maps
Weighted average taken as
Proxy for probability of flood
Real-time flood mapping
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• Twitter Data
5 Min resolution
Water depth
Location
• February 5th 2014
Peak 250/5
minutes
Probabilistic approach
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P = 0.3
P = 0.5
P = 0.2
x
x
x
Results 10 Feb 2015
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York floods 2015: deterministic approach
29 m
0 m0 5 102.5 Km
Generate flood plane
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• Group observations by flowpath
• Interpolate water levels on reach
! Observation0 3 61.5 Km
29 m
0 m0 5 102.5 Km
York floods, 2015
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[1]
[3]
[5]
[2]
[6]
[9]
[11]
[10]
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0 1 20.5 Km
Recorded Flood Extent
Mapped Water depth (m)
5 m 0 m
[4][1]
[3]
[5]
[2]
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0 0.1 0.20.05 Km
Recorded Flood Extent
Mapped Water depth (m)
5 m 0 m
Uncertainty analysis
Uncertainty due to:
• Locational errors
• Elevation errors
• Water depth error
• Total uncertainty
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[1] [2]
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! Validation Points
Mapped Water Depth
5 m 0 m
0 5 102.5 Km
Percentage Flooded
100% 50% 0%
0 5 102.5 Km
Percentage Flooded
100% 50% 0%
0 5 102.5 Km
Percentage Flooded
100% 50% 0%
0 5 102.5 Km
Percentage Flooded
100% 50% 0%
0 5 102.5 Km
Flood probability
Concluding
• There is a lot of useful information out there
• The information is real-time and (partly) publicly available
• The challenge is filtering out the useful data; real-time hydrological
data and tools can help
• Uncertainty in flood mapping due to location and observation error
• Quality of flood mapping increases by using hydrological
characteristics
• Useful flood maps can be generated using a low number of
observations
• Photos contain a lot of useful information
Questions?
Ideas for further development and possible
applications in projects are also welcome!
[email protected] +31(0)88335 8525
[email protected] +31(0)88335 7672
