DSD-INT 2016 Real-time flood extent maps based on social media - Van Loenen
•
1 like•325 views
Presentation by Arnejan van Loenen (Deltares), Dirk Eilander (Deltares), Patricia Trambauer (Deltares) and Jurjen Wagemaker (FloodTags.com) at Earth Observation and Data Science Symposium, during Delft Software Days 2016. Monday 24 October 2016, Delft.
Recommended
Recommended
More Related Content
Viewers also liked
Viewers also liked (10)
Similar to DSD-INT 2016 Real-time flood extent maps based on social media - Van Loenen
Similar to DSD-INT 2016 Real-time flood extent maps based on social media - Van Loenen (17)
More from Deltares
More from Deltares (20)
Recently uploaded
Recently uploaded (20)
DSD-INT 2016 Real-time flood extent maps based on social media - Van Loenen
- 1. Real-time flood extent maps based on social media Arnejan van Loenen Dirk Eilander Patricia Trambauer Jurjen Wagemaker (FloodTags.com) Email: arnejan.vanloenen@deltares.nl
- 2. FloodControl 2015 – Dashboard Banjir Online
- 3. Pilot area: Jakarta 3 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”
- 4. Flood observations in the “Twitter cloud” Data integration platform: Delft-FEWS
- 6. 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
- 7. Information sources during floods Telemetry CCTV Rain radar Remote Sensing
- 8. Potential use of social media 8 -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
- 9. How to find the useful information 9
- 10. 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 10
- 11. Is it a flood prone area? Method 2: Slope 11
- 12. Is it a flood prone area? 12 • 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
- 13. What is heavy rain? 13 90th percentile 99th percentile Local hourly precipitationAccumulated daily precipitation
- 14. Contextual validation method Static maps Dynamic maps Weighted average taken as Proxy for probability of flood
- 15. Real-time flood mapping 15 • Twitter Data 5 Min resolution Water depth Location • February 5th 2014 Peak 250/5 minutes
- 16. Probabilistic approach 16 P = 0.3 P = 0.5 P = 0.2 x x x
- 17. Results 10 Feb 2015 17
- 18. York floods 2015: deterministic approach 29 m 0 m 0 5 102.5 Km
- 19. Generate flood plane 19 • Group observations by flowpath • Interpolate water levels on reach ! Observation0 3 61.5 Km 29 m 0 m 0 5 102.5 Km
- 20. York floods, 2015 20 [1] [3] [5] [2] [6] [9] [11] [10] [8] [7] [4] 0 1 20.5 Km Recorded Flood Extent Mapped Water depth (m) 5 m 0 m [4] [1] [3] [5] [2] [6] [9] [11] [10] [8] [7] 0 0.1 0.20.05 Km Recorded Flood Extent Mapped Water depth (m) 5 m 0 m
- 21. Uncertainty analysis Uncertainty due to: • Locational errors • Elevation errors • Water depth error • Total uncertainty 21 [1] [2] [3] [4] [5] ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 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
- 22. 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
- 23. Questions? Ideas for further development and possible applications in projects are also welcome! Arnejan.vanLoenen@deltares.com +31(0)88335 8525 Dirk.Eilander@delteres.nl +31(0)88335 7672