1. Optimisation of flood risk management strategies - Developments in FRMRC Michelle Woodward 1,2, Ben Gouldby 1 and Zoran Kapelan 2 International workshop on the science of asset management 9 th December 2011 1 HR Wallingford 2 University of Exeter

2. Page 2 Presentation Overview - Decision Support system overview - Description of each component -Flood risk management intervention strategies -Risk analysis model -Cost Model -Optimisation Algorithm -Decision support - Case study on the Thames Estuary

3. Page 3 Decision Support System

4. Page 4 Decision Support System INPUT Intervention Strategy constraints: - Length of intervention strategy (e.g. 10yrs, 15yrs, 20yrs…) - Number of time steps (e.g. 1, 2, 3…) - Length of time steps (e.g. 5yrs, 10yrs …) - Types of intervention measures (e.g. Structural interventions, flood proofing) - Constraints between time steps (e.g. Account for previous epochs) - Constraints to ensure realistic measures (e.g. max height increase) Selection of Objective Functions - Single objective (e.g. NPV, BCR) - Multi objective (e.g. Benefit, Cost, Loss of life …)

5. Page 5 Decision Support System

6. Page 6 Utilises a structured definition of the flood system Flood risk model (For a more detailed description see Hall et al 2003., and Gouldby et al 2008.)

7. Page 7 Intervention measures implemented in risk model SOURCE PATHWAY RECEPTOR Raise crest level of defence Widen base of defences Set back defences Defence maintenance Flood proof properties Flood warnings Climate Change Scenarios Socio Economic Scenarios

8. Page 8 Model approximation – replaced Monte-Carlo simulation with an average volume approach Number of inundation simulations from: >20,000 goes to 5 Ok For optimisation? Simplified risk model 97.97%

9. Page 9 Decision Support System

10. Page 10 Cost Model

11. Page 11 Cont…

12. Page 12 Decision Support System

13. Page 13 Optimisation Algorithms Optimisation techniques are beneficial in flood risk management because - they can handle a large portfolio of possible intervention options at different sequences through time - they can give consideration to multiple conflicting objectives

14. Page 14 Evolutionary Algorithms - Powerful Search Process - Based on Darwin’s Theory of Natural Selection and survival of the fittest - Methods include: -Genetic Algorithms -Shuffled Complex Evolution -Ant Colony Optimisation -Multi-Objective Genetic Algorithm

15. Page 15 Genetic Algorithm Generate initial population START Evaluate objective function Are optimisation criteria met? Best individual RESULT Generate new population Mutation CrossoverSelection Single Objective Optimisation: Maximise NPV or Maximise BCR Multiobjective optimisation: Maximise Benefits and Minimise Costs

16. Page 16 Decision Support System

17. Page 17 Decision Support OUTPUT Single Objective Optimisation: - Single Optimal Intervention strategy - Optimised according to chosen objective Multi Objective Optimisation: - A trade off curve (Pareto Front) of the conflicting criteria - A set of optimal intervention strategies to support decision makers

18. Page 18 The Pareto Front

19. Page 19 The Pareto Front

20. Page 20 The Pareto Front

21. Page 21 Case Study on the Thames Estuary Westminster Greenwich Tilbury River Lee River Roding Purfleet Gravesend Richmond Legend Tidal flood risk area Tidal flood defences

22. Page 22 Flood Defence Examples 1. Concrete vertical wall 2. Embankment 3. Sheet-pile vertical wall 1. 2. 3.

23. Page 23 Results StrategyBenefit £ 000’s Cost £ 000’s NPV £ 000’s BCR# of people at risk A774.867.8304.75.49940 B1211.195.81115.312.6424,710 C1570.8231.51339.36.7818,860 D1622.8327.31295.55.705,435 E1643.41521.9121.510.8785

24. Page 24 Summary/conclusions - Risk models are useful decision support tools - These models can be simplified for use in optimisation analysis - Multi-objective optimisation techniques can provide more information to decision makers - Multi-objective optimisation techniques are useful tools to automate the search process given a large range of potential options - Need to incorporate a greater range of consequences in risk models, loss of life (hence benefits of flood warning), environmental impacts etc.

25. Page 25 Dissemination of work Woodward, M., Gouldby, B., Kapelan, Z., Khu, S. T. & Townend, I. (2011) Real Options in Flood Risk Management Decision Making. Journal of Flood Risk Management, 4, 339-349. Woodward, M., Gouldby, B., Kapelan, Z. & Hames, D. (2011) Multiobjective Optimisation for Improved Management of Flood Risk. ASCE Journal of Water Resources Planning and Management, (In Review). Woodward, M., Kapelan, Z. & Gouldby, B. (2011) Developing Flexible and Adaptive Flood Risk Management Options Based on a Real Options Decision Tree Approach. (In progress)