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DSD-INT 2019 How machine learning will change flood risk and impact assessments - Wagenaar

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Presentation by Dennis Wagenaar, Deltares, at the Data Science Symposium, during Delft Software Days - Edition 2019. Thursday, 14 November 2019, Delft.

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DSD-INT 2019 How machine learning will change flood risk and impact assessments - Wagenaar

  1. 1. How machine learning will change flood risk and impact assessments Dennis Wagenaar
  2. 2. • Group vision paper • Based on brainstorm • Collaboration between: • Universities • World Bank • Startups • Deltares Understanding risk field lab on urban flooding
  3. 3. Innovation required! Global yearly impacts natural hazards: - US$ 291 billion - 130 000 killed - 440 million affected
  4. 4. Machine Learning Machine learning • Major impact on many sectors • Better data, better algorithms • In Flood risk and impact assessments?
  5. 5. Machine learning in flood risk and impact assessments Not completely new: • Hydroinformatics • Remote sensing • Could be applied more Much more potential!
  6. 6. Where can we apply it?
  7. 7. Where can we apply it? Predictive Descriptive Exposure Urban growth modelling Identification current build-up Hazard Flood modelling Mapping current and past floods Impact Flood impact modelling Assessing flood impacts
  8. 8. How machine learning works Indicators Response (variable of interest) Trainingdata Historical records of indicators (e.g. rainfall, wind speed, building data) Historical records of response (e.g. damage) Applicationdata Indicators data new case (e.g. rainfall, wind speed, building data) Response new case (e.g. damage)
  9. 9. A machine learning algorithm you may already know: Linear regression X: Indicator data (e.g. water level) Y: Response (e.g. damage) Blue dots: Historical data Red line: Model New predictions made by looking up y for a given x
  10. 10. Example of more advanced machine learning algorithm: Decision trees Well known algorithms • Linear regression • Multi-variable linear regression • Polynominal regression • Logistic regression • Decision/regression tree • Random Forest • Artificial Neural Networks (ANN) • Convolutional Neural Network (CNN) • Support Vector Machines (SVM) • Bayesian Networks
  11. 11. • Very good physics based models • Machine learning can’t deal with system change • Useful for forecasting frequent events • Sometimes better or cheaper • Surrogate models • Google: Automatic calibration based on remote sensing data Predictive hazard: Modelling the water
  12. 12. • Social media (e.g. twitter) • Flood mapping • Water depths from photos • Remote sensing • Optical data – cloud cover/night • Synthetic Aperture Radar (SAR) • Automatically label floods Descriptive hazard: Observing floods
  13. 13. From the air • Global building footprints • Global road information • Should become available at one point soon. From the ground • 360 degree street view • Building materials • Building entrance heights Descriptive exposure: Automatic detection 360 degree street view images
  14. 14. • Predicting impact • Predict flood damage • Predict health impacts • Predict flood casualties • Predict required resources • Already done, lack of data • Exposure data could become available Predictive impacts: The final step
  15. 15. • Machine learning raises ethics and bias questions • Automatic weaponry • Facial recognition • Aggravating inequalities through biased training sets • Increased complexity • Misuse • Lack of uncertainty communication • Overhyped • Working group, guideline Ethics and bias
  16. 16. Model chains and machine learning Wind speed Surge model Overland flow model Flood damage model Traditional chain Hybrid chain Pure machine learning Wind speed Surge model Overland flow model ML flood damage model Wind speed Distance to shore Elevation ML flood damage model Damage Damage Damage Key is making the right choice per component
  17. 17. Machine learning methods vs traditional methods (1) Exact known relationships Complex processes with many variables Use formulas based on physics Consider data- driven methods
  18. 18. Machine learning methods vs traditional methods (2) Extrapolation or system changes No extrapolation or system changes Use formulas based on physics Consider data- driven methods
  19. 19. What will become possible Better modelsNew applications • Targeted early actions • Early harvesting crops • Strengthening buildings • Quick estimates of recovery needs • Parametric insurance
  20. 20. More information:

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