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
Real Options, Optimisation Methods and Flood Risk Management Michelle Woodward - HR Wallingford and Exeter University Ben Gouldby – HR Wallingford Zoran.
Dec 16, 2015
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Real Options, Optimisation Methods and Flood Risk Management
Michelle Woodward - HR Wallingford and Exeter UniversityBen Gouldby – HR Wallingford
Zoran Kapelan – Exeter UniversitySoon-Thiam Khu – Exeter University
Michelle Woodward - HR Wallingford and Exeter UniversityBen Gouldby – HR Wallingford
Zoran Kapelan – Exeter UniversitySoon-Thiam Khu – Exeter University
Page 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 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 4
Background to RASP
Risk Assessment for System Planning
Research Project funded by the UK Environment
Agency (2001-2004)
Page 5
RASP is a framework for flood risk analysis
Common database (NFCDD)
Common input/output
Catchment / Coastal Cell LevelCatchment / Coastal Cell LevelStrategic planningDevelopment regulation
Site / System LevelScheme appraisalSite / System Level
National Level-
National Level
National justification, regional prioritisation, long term outlook -
Page 6
Conceptual model
Utilises a structured definition of the flood system
Page 7
The system model
Determining flood depth versus probability
Pathway
Source
Receptor
Pathway
Source
Receptor
Source
ReceptorThe 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
Page 8
Pathway
Source
Receptor
Pathway
Source
Receptor
Source
Receptor
Model has been compared to hydrodynamic models like Infoworks-RS2D
All inundation scenariosA new super fast inundation model (HR RSFM) enables 10000s of inundation scenarios to be realisedRuntime: <0.1 sec
Page 9
The system model
Estimating flood damagesThree steps are used to calculate risk
1. Depth damage curves are used to assess the damage associated with each possible flood scenario
2. By combining the scenario damage with the probability of the scenario occurring a scenario risk is estimated
3. By integrating across all scenarios the expected annual damages (risk) is determined
Depth Damage Curve
-1.00-0.75-0.50-0.250.000.250.500.751.001.251.501.752.002.252.502.753.00
0 250 500 750 1000 1250 1500
Damage £/m2
Dep
th M
etre
s
HighSusceptibilityBand
LowSusceptibilityBand
IndicativeSusceptibility
Source: Flood Hazard Research Centre, 2003
Page 10
Investigating intervention strategies
Risk profile through time for HLO 1, 2 and the P3 Policy
0
10
20
30
40
50
60
2000 2020 2040 2060 2080 2100 2120
Time (year)
Ris
k(E
AD
£m
)
P3
HLO1
Page 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 12
Structure of a Simple Genetic Algorithm
START
Generate initial
population
ApplicationModel
Evaluateobjectivefunction
Areoptimisation
criteriamet?
Bestindividual
RESULT
SelectionCrossoverMutation
Generate new population
Page 13
Genetic Algorithm Operators
5 2 4 6 7 1 8 Two Parent Chromosomes
6 9 3 1 4 2 0
6 9 3 1 7 1 8
5 2 4 6 4 2 0
5 2 4 6 4 2 0
6 9 9 1 7 1 8
Two new OffspringMutation
Crossover
Page 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 15
The Pareto Front
Ob
ject
ive
2 (
to b
e m
inim
ise
d)
Objective 1 (to be minimised)
Page 16
The Pareto Front
Ob
ject
ive
2 (
to b
e m
inim
ise
d)
Objective 1 (to be minimised)
Page 17
The Pareto Front
Ob
ject
ive
2 (
to b
e m
inim
ise
d)
Objective 1 (to be minimised)
Page 18
Optimisation Problem
Objectives:
Maximise Benefit:
EADwithout interventions – EADwith interventions
n
Minimise total cost: ∑Ci Ci = costs per intervention i = 1
Subject to: Realistic and available intervention options
Page 19
Cost (£’s)
Benefit (£’s)
The Pareto Front
Identification of most appropriate option/s given
fixed budget
Identification of costs associated
with specified benefit level
Identification of transition, where significantly more investment yields little
benefit (incremental benefit cost)
Multi-objective optimisation
Page 20
Real options overview
“A Real Option is a choice that becomes available through an investment opportunity or action”
Page 21
Real Option Overview
Current Defence
Maximum height increase for current defence
Maximum height increase for
widened defence
Widening of Base
Present Day extreme water level
Plausible range of future extreme
water levels
Page 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 23
Generate a (Real) option(Optimisation method)
Calculate NPV BenefitsMultiple futures
(fastRASP)
Calculate NPV cost(Cost functions)
Calculate option fitness for:Multiple objectives
Multiple futures(Single decision method)
optimum solutions found
No
Yes
Output Pareto Set of optimum
solutions
Overview of framework
Decision variables include:
Standard of maintenance
Raise crest level (Each defence)
Widen defence (each defence)
Non structural measures (flood proofing)
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