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Spatial Optimization based on integrated impact of climate change Supported by MOTIVE project Eun Joo Yoon, Donk Kun Lee Seoul National University
Spatial Optimization based on integrated impact
of climate change
Supported by MOTIVE project
Eun Joo Yoon, Donk Kun Lee
Seoul National University
Seoul National University
Introduction
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Motive project
Model Of InTegrated Impact and Vulnerability Evaluation
of Climate Change
The Title of project is:
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Motive project
Sponsor: Ministry of Environment Project Period: 2014.5.1~2021.4.30(7 Years) 100+Experts from the Interdisciplinary Research groups
Ultimate Goal: Development of integrated evaluation model reflecting Korean circumstance to be utilized for designing ‘science-based adaptation strategies’
Scopes
Time period: 2030s, 2050s, 2080s Spatial : S. Korea 1kmx1km
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Motive projectImpact team2
Agriculture
Impact map Impact
model
IntegratedModel team
IntegratedRiskteam
IntegratedImpact team
Inter sectoral integrated impact
sectoral impact
Ocean/Fishery Health Water Forest Agriculture Ecosystem
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Integrated team
Forest fire
Landslide
Agriculture
Flood
Safetymanagement
Provisioningservices
Species
Land use planning
Protectedarea
Complex
DisasterSelection of
alternatives
for achieving
environmental
policies
Land use
Allocation
Impact by C.C. Integrated impact Environmental policy
Water security
Conservation
Individual impact maps
Inter sectoral integrated impact
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Objective of land use allocation
Implementation on real space is another problem
“To suggest optimized land use allocation
Considering integrated impacts”
Climate change
Policies on adaptation
To support decision making
How should we change our space?
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Objective of land use allocation
Land useOptimization
Land usePrediction
objectives
Impact maps
Planning
Seoul National University
Method
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Why we use optimization
Making plans
?????MakingPlans!!
Traditional planning Planning considering climate change
data1
data2data3
data4
data4
data2
data3
data1
impact1
Impact3
impact2
Impact4
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Why we use optimization
Environmental values
Socialvalues
Economical values
Various values are
competing each other
Only one real space
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Why we use optimization
Impact 1: high water security
a
Suitable for agriculture
Impact 2: high conservation value
Suitable for forest
impact 3: low disaster possibility
Suitable for development
b
“What impact has priority?
->There is no reasonable basis to decide.
->There is large number of cases.
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Why we use optimization
We can solve this problem using optimization algorithm!!
Local optimum
Local optimum
Global optimum
Most popular and effective optimization tool for the spatial planning is Genetic Algorithm
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Traditional Genetic Algorithm
Crossover
Mutation
Change some part of parent,->Select better individuals,
->Go to the next generation.
1
2
3
4
5
Final
Parent Parent
Offspring Offspring
Offspring Offspring
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Our model
TraditionalGA
OperatorFor spatial
compactness
NSGA2(Dep et al., 2002)
Our model
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Crossover/Mutation operatorSpecial operator for spatial optimizationTraditional crossover
Two Parents
Two Offspring
One Parent One Offspring
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NSGA2Previous
Generation
Crossover
Mutation
rank1
rank2
rank3
Next
GenerationCD
obj2
obj1
Pareto Front
Non Dominated Solution: rank1
Direction of
optimization
Crowding Distance
Dominated Solution: rank2
A
B
C
Seoul National University
Case study
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Study area
Pyeongchang gun, Korea
High landslide susceptibility, further increasing is expected (climate change)
Fast land use change: High development pressure owing to new trail under construction, winter Olympic
How can we reduce or prevent of landslide damages
considering climate change??
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Landslides(Current & Future)
Landslide susceptibility 2006 Landslide susceptibility 2071-2099
RCP 8.5 Scenario
High
Low
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Objectives and constraints
Minimization of Landslides damages Minimization of Change
Maximization of Compactness
ConstraintDevelopment area increase
No development above than 800m
(Risk Matrix) Relative sore
Spatial objective
Considering 8 boundary cells
Suscepti
bility
devlope
ment
Agricult
ure자연
5 High
4 Medium
3 Low
2
1
k k k m
k k k m
k k k l
m l l l
m l k m
m m m m
민감도 개발지 농경지 자연
개발지 0 1 1
농경지 0.6 0 0.2
자연 0.7 0.4 0
Seoul National University
Results
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Pareto setLandslide susceptibility 2006
Landslide susceptibility 2071-2099(RCP 8.5)
Competing/trade off
Optimized
Alternatives(Climate change)Alternative1 Alternative2 Alternative3
0Which is good for our condition?
Alternative4 Alternative5 Alternative6 Alternative7
Plans Objective1 Objective2 Objective3
Current 1,023 504 203
Alternative1 8,123 408 107
Alternative2 9.987 397 187
Alternative3 7,583 493 201
Alternative4 … … …
Alternative5 … … …
Alternative6 … … …
Alternative7 … … … 24
Alternatives
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Alternatives(Our results)
Decision
makers,
Planners
select
Feedback
(parameter..)
Further analysis,
Detailed
planning
yes
no
Seoul National University
Discussions
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Limitations
But, we have to improve:
Optimization level
Objective functions
Computational time
We believe this model can support decision making, “action for climate change”,
Adaptationpolicies
Real space
Our research
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Flexible structure
Initialization`
Crossover/Mutation
Selection
OptimizedResults
Positive RoofObjective ObjectiveObjective
Inputdata
Inputdata
Inputdata
Function Function Function
Can be changed, can solve another problems
