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Real Options, Optimisation Methods and Flood Risk Management

Real Options, Optimisation Methods and Flood Risk Management. Michelle Woodward - HR Wallingford and Exeter University Ben Gouldby – HR Wallingford Zoran Kapelan – Exeter University Soon-Thiam Khu – Exeter University. Objective of PhD. Objective:

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Real Options, Optimisation Methods and Flood Risk Management

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  1. Real Options, Optimisation Methods and Flood Risk Management Michelle Woodward - HR Wallingford and Exeter University Ben Gouldby – HR Wallingford Zoran Kapelan – Exeter University Soon-Thiam Khu – Exeter University

  2. Objective of PhD • Objective: • To investigate optimum flood risk intervention strategies taking into account the possible effects of climate change Title:Real options based optimum selection of flood risk mitigation options Page 2

  3. Presentation outline • Overview of Risk Analysis tool • Calculating Benefits of interventions • Optimisation Techniques • Evolutionary Algorithms • Dynamic Programming • Real Options • Valuing flexibility for climate change adaptation strategies • Outline of computational framework Page 3

  4. Background to RASP • Risk Assessment for System Planning • Research Project funded by the UK Environment • Agency (2001-2004) Page 4

  5. National Level National Level - - National justification, regional prioritisation, long term outlook Common database (NFCDD) Common input/output Catchment / Coastal Cell Level Catchment / Coastal Cell Level Strategic planning Development regulation Site / System Level Site / System Level Scheme appraisal RASP is a framework for flood risk analysis Page 5

  6. Conceptual modelUtilises a structured definition of the flood system Page 6

  7. The system model: • Recognises that levees behave as “defence systems” • A flood depth versus probability distribution is established by considering multiple combinations of storm loading and possible levee failure The system modelDetermining flood depth versus probability Page 7

  8. All inundation scenarios A new super fast inundation model (HR RSFM) enables 10000s of inundation scenarios to be realised Runtime: <0.1 sec Model has been compared to hydrodynamic models like Infoworks-RS2D Page 8

  9. Three steps are used to calculate risk Depth damage curves are used to assess the damage associated with each possible flood scenario By combining the scenario damage with the probability of the scenario occurring a scenario risk is estimated By integrating across all scenarios the expected annual damages (risk) is determined Source: Flood Hazard Research Centre, 2003 The system modelEstimating flood damages Page 9

  10. Investigating intervention strategies Page 10

  11. Optimisation Techniques • Dynamic Programming Enumerative Scheme • Evolutionary Algorithms Inspired by Darwin’s theory of evolution Survival of the fittest Genetic operators • Reproduction (crossover) • Mutation • Selection Page 11

  12. START Are optimisation criteria met? Generate initial population Application Model Evaluate objective function Best individual Generate new population RESULT Mutation Crossover Selection Structure of a Simple Genetic Algorithm Page 12

  13. Two new Offspring Genetic Algorithm Operators 5 2 4 6 7 1 8 Two Parent Chromosomes 6 9 3 1 4 2 0 5 2 4 6 4 2 0 Crossover 6 9 3 1 7 1 8 Mutation 5 2 4 6 4 2 0 6 9 9 1 7 1 8 Page 13

  14. Multi-objective optimisation • Multi objective optimisation methods seek solutions that are “optimum” with respect to all objectives. • Invariably a set of optimal solutions is discovered (known as a Pareto set) Page 14

  15. The Pareto Front Page 15

  16. The Pareto Front Page 16

  17. The Pareto Front Page 17

  18. Optimisation Problem • Objectives: Maximise Benefit: EADwithout interventions – EADwith interventions n Minimise total cost: ∑CiCi = costs per intervention i = 1 Subject to: Realistic and available intervention options Page 18

  19. Identification of transition, where significantly more investment yields little benefit (incremental benefit cost) Identification of costs associated with specified benefit level The Pareto Front Benefit (£’s) Identification of most appropriate option/s given fixed budget Cost (£’s) Multi-objective optimisation Page 19

  20. Real options overview • “A Real Option is a choice that becomes available through an investment opportunity or action” Page 20

  21. Maximum height increase for widened defence Plausible range of future extreme water levels Maximum height increase for current defence Present Day extreme water level Current Defence Widening of Base Real Option Overview Page 21

  22. Framework for Optioneering • Features include • Analysis of Real Options • Automated option searching techniques using evolutionary optimization processes (multi-objective optimization) • Automated option cost generation • Economic discounting of benefits and costs • Temporally evolving risk analysis (a fastRASP) – risk is a function of future climate change scenario, future socio-economic scenarios • Range of decision making methods Page 22

  23. Overview of framework Decision variables include: Standard of maintenance Raise crest level (Each defence) Widen defence (each defence) Non structural measures (flood proofing) Page 23

  24. Thank you for listening • m.woodward@hrwallingford.co.uk • b.gouldby@hrwallingford.co.uk Page 24

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