K.Fedra ‘97 Spatial DSS environmental applications of spatial decision support systems.
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Transcript of K.Fedra ‘97 Spatial DSS environmental applications of spatial decision support systems.
K.Fedra ‘97
Spatial DSSSpatial DSSSpatial DSSSpatial DSS
environmental applications of environmental applications of spatial decision support spatial decision support
systemssystems
environmental applications of environmental applications of spatial decision support spatial decision support
systemssystems
K.Fedra ‘97
Spatial DecisionsSpatial DecisionsSpatial DecisionsSpatial Decisions
Spatial decisions:Spatial decisions:
• Set of criteriaSet of criteria– objectives
– constraints
are functions of spaceare functions of space
Spatial decisions:Spatial decisions:
• Set of criteriaSet of criteria– objectives
– constraints
are functions of spaceare functions of space
K.Fedra ‘97
Spatial DecisionsSpatial DecisionsSpatial DecisionsSpatial Decisions
Spatially distributed systems can be Spatially distributed systems can be represented by spatially distributed represented by spatially distributed models.models.
Modeling is used to Modeling is used to • design a design a set of alternativesset of alternatives to to
choose from (simulation models)choose from (simulation models)• design an design an optimal alternativeoptimal alternative
(optimisation models)(optimisation models)
Spatially distributed systems can be Spatially distributed systems can be represented by spatially distributed represented by spatially distributed models.models.
Modeling is used to Modeling is used to • design a design a set of alternativesset of alternatives to to
choose from (simulation models)choose from (simulation models)• design an design an optimal alternativeoptimal alternative
(optimisation models)(optimisation models)
K.Fedra ‘97
Why Modeling:Why Modeling:Why Modeling:Why Modeling:
• conceptualising, organisingconceptualising, organising
• communicatingcommunicating
• understanding, assessing understanding, assessing
• testing field measurementstesting field measurements
• forecasting, early warningforecasting, early warning
• optimising decision makingoptimising decision making
• conceptualising, organisingconceptualising, organising
• communicatingcommunicating
• understanding, assessing understanding, assessing
• testing field measurementstesting field measurements
• forecasting, early warningforecasting, early warning
• optimising decision makingoptimising decision making
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
• Atmospheric systemsAtmospheric systems• Hydrologic systemsHydrologic systems• Land surface and subsurfaceLand surface and subsurface• Biological and ecological systemsBiological and ecological systems• Risks and hazardsRisks and hazards• Technological systemsTechnological systems• Management and policy modelsManagement and policy models
• Atmospheric systemsAtmospheric systems• Hydrologic systemsHydrologic systems• Land surface and subsurfaceLand surface and subsurface• Biological and ecological systemsBiological and ecological systems• Risks and hazardsRisks and hazards• Technological systemsTechnological systems• Management and policy modelsManagement and policy models
K.Fedra ‘97
Structuring the problemStructuring the problemStructuring the problemStructuring the problem
• problem statement (description)problem statement (description)• criteria criteria (measurable attributes)(measurable attributes)
• objectives objectives (minimise, maximise)(minimise, maximise)
• constraintsconstraints (inequalities) (inequalities)
• contextcontext
• problem statement (description)problem statement (description)• criteria criteria (measurable attributes)(measurable attributes)
• objectives objectives (minimise, maximise)(minimise, maximise)
• constraintsconstraints (inequalities) (inequalities)
• contextcontext
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Atmospheric systemsAtmospheric systems• weather forecastingweather forecasting
• climate modelsclimate models
• air pollution: industry, traffic, air pollution: industry, traffic, domestic sources, accidental domestic sources, accidental releases (hazardous substances)releases (hazardous substances)
Atmospheric systemsAtmospheric systems• weather forecastingweather forecasting
• climate modelsclimate models
• air pollution: industry, traffic, air pollution: industry, traffic, domestic sources, accidental domestic sources, accidental releases (hazardous substances)releases (hazardous substances)
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Air pollution controlAir pollution control• impacts and hazardsimpacts and hazards
– human end environmental exposurehuman end environmental exposure
– damage through explosion and firedamage through explosion and fire
– damage through chemical reactionsdamage through chemical reactions (corrosion)(corrosion)
Air pollution controlAir pollution control• impacts and hazardsimpacts and hazards
– human end environmental exposurehuman end environmental exposure
– damage through explosion and firedamage through explosion and fire
– damage through chemical reactionsdamage through chemical reactions (corrosion)(corrosion)
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Hydrologic systemsHydrologic systems• hydrological cycle, rainfall-runoffhydrological cycle, rainfall-runoff• river flow and floodingriver flow and flooding• water distribution and allocationwater distribution and allocation• reservoir operationsreservoir operations• water quality, eutrophication, water quality, eutrophication, waste allocationwaste allocation• groundwater systemsgroundwater systems
Hydrologic systemsHydrologic systems• hydrological cycle, rainfall-runoffhydrological cycle, rainfall-runoff• river flow and floodingriver flow and flooding• water distribution and allocationwater distribution and allocation• reservoir operationsreservoir operations• water quality, eutrophication, water quality, eutrophication, waste allocationwaste allocation• groundwater systemsgroundwater systems
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Coastal waters and oceansCoastal waters and oceans• currents and energy balance currents and energy balance
(climate modeling)(climate modeling)
• coastal water qualitycoastal water quality
• nutrient cycles, eutrophicationnutrient cycles, eutrophication
• fisheries (sustainable yield)fisheries (sustainable yield)
Coastal waters and oceansCoastal waters and oceans• currents and energy balance currents and energy balance
(climate modeling)(climate modeling)
• coastal water qualitycoastal water quality
• nutrient cycles, eutrophicationnutrient cycles, eutrophication
• fisheries (sustainable yield)fisheries (sustainable yield)
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Land surface and subsurfaceLand surface and subsurface• erosion, soil processeserosion, soil processes
• vegetation, land covervegetation, land cover
• groundwater (unsaturated and groundwater (unsaturated and saturated zones, links to the saturated zones, links to the hydrological domain)hydrological domain)
Land surface and subsurfaceLand surface and subsurface• erosion, soil processeserosion, soil processes
• vegetation, land covervegetation, land cover
• groundwater (unsaturated and groundwater (unsaturated and saturated zones, links to the saturated zones, links to the hydrological domain)hydrological domain)
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Biological and ecological systemsBiological and ecological systems• population models, predator-prey population models, predator-prey
systems, food chainssystems, food chains
• ecosystem models (multi-ecosystem models (multi-compartment combining physical compartment combining physical and biological elements)and biological elements)
Biological and ecological systemsBiological and ecological systems• population models, predator-prey population models, predator-prey
systems, food chainssystems, food chains
• ecosystem models (multi-ecosystem models (multi-compartment combining physical compartment combining physical and biological elements)and biological elements)
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Agriculture and ForestryAgriculture and Forestry• agricultural production agricultural production
• livestock and grazing modelslivestock and grazing models
• forest models (stands, growth, yield, forest models (stands, growth, yield, deforestation and reforestation)deforestation and reforestation)
Agriculture and ForestryAgriculture and Forestry• agricultural production agricultural production
• livestock and grazing modelslivestock and grazing models
• forest models (stands, growth, yield, forest models (stands, growth, yield, deforestation and reforestation)deforestation and reforestation)
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Technological systemsTechnological systems
• transportationtransportation
• energy systemsenergy systems
• industrial impactsindustrial impacts
• waste managementwaste management
Technological systemsTechnological systems
• transportationtransportation
• energy systemsenergy systems
• industrial impactsindustrial impacts
• waste managementwaste management
K.Fedra ‘97
Modeling DomainsModeling DomainsModeling DomainsModeling Domains
Risks and hazardsRisks and hazards• floods and droughtsfloods and droughts
• erosion, desertificationerosion, desertification
• spills and accidental releasesspills and accidental releases
• epidemiological models (pests, epidemiological models (pests, infectious diseases)infectious diseases)
Risks and hazardsRisks and hazards• floods and droughtsfloods and droughts
• erosion, desertificationerosion, desertification
• spills and accidental releasesspills and accidental releases
• epidemiological models (pests, epidemiological models (pests, infectious diseases)infectious diseases)
K.Fedra ‘97
Spatial decisionsSpatial decisionsSpatial decisionsSpatial decisions
Environmental decision are also Environmental decision are also spatial decisions:spatial decisions:
• site selection, locationsite selection, location• pollution controlpollution control• natural resources managementnatural resources management• environmental impact assessmentenvironmental impact assessment• risk analysis and managementrisk analysis and management
Environmental decision are also Environmental decision are also spatial decisions:spatial decisions:
• site selection, locationsite selection, location• pollution controlpollution control• natural resources managementnatural resources management• environmental impact assessmentenvironmental impact assessment• risk analysis and managementrisk analysis and management
K.Fedra ‘97
Spatial decisionsSpatial decisionsSpatial decisionsSpatial decisions
site selection, locationsite selection, location
• site selection for special activities or site selection for special activities or installations (power plants, incinerators, installations (power plants, incinerators, hazardous waste facilities): NIMBYhazardous waste facilities): NIMBY
• site suitability analysissite suitability analysis
• zoning, land use managementzoning, land use management
site selection, locationsite selection, location
• site selection for special activities or site selection for special activities or installations (power plants, incinerators, installations (power plants, incinerators, hazardous waste facilities): NIMBYhazardous waste facilities): NIMBY
• site suitability analysissite suitability analysis
• zoning, land use managementzoning, land use management
K.Fedra ‘97
Spatial decisionsSpatial decisionsSpatial decisionsSpatial decisions
pollution controlpollution control
• commissioning of sourcescommissioning of sources
• resource allocation to source controlresource allocation to source control
• incentives and taxes for emission incentives and taxes for emission sourcessources
• clean-up strategiesclean-up strategies
pollution controlpollution control
• commissioning of sourcescommissioning of sources
• resource allocation to source controlresource allocation to source control
• incentives and taxes for emission incentives and taxes for emission sourcessources
• clean-up strategiesclean-up strategies
K.Fedra ‘97
Spatial decisionsSpatial decisionsSpatial decisionsSpatial decisions
natural resources managementnatural resources management• harvest and management strategies harvest and management strategies
(maximum sustainable yield) for forestry, (maximum sustainable yield) for forestry, fisheries, livestockfisheries, livestock
• land-use (crop) allocationland-use (crop) allocation• commissioning (mining, extraction) commissioning (mining, extraction) • land reclamation, site remediationland reclamation, site remediation• water resources management, water water resources management, water
allocationallocation
natural resources managementnatural resources management• harvest and management strategies harvest and management strategies
(maximum sustainable yield) for forestry, (maximum sustainable yield) for forestry, fisheries, livestockfisheries, livestock
• land-use (crop) allocationland-use (crop) allocation• commissioning (mining, extraction) commissioning (mining, extraction) • land reclamation, site remediationland reclamation, site remediation• water resources management, water water resources management, water
allocationallocation
K.Fedra ‘97
Spatial decisionsSpatial decisionsSpatial decisionsSpatial decisions
environmental impact assessmentenvironmental impact assessment
• scoping and screeningscoping and screening
• impact assessment for major impact assessment for major development projectsdevelopment projects
• policy assessmentpolicy assessment
environmental impact assessmentenvironmental impact assessment
• scoping and screeningscoping and screening
• impact assessment for major impact assessment for major development projectsdevelopment projects
• policy assessmentpolicy assessment
K.Fedra ‘97
Spatial decisionsSpatial decisionsSpatial decisionsSpatial decisions
risk analysis and managementrisk analysis and management• siting and commissioning of hazardous siting and commissioning of hazardous
installationsinstallations• operational managementoperational management• hazardous substances and waste hazardous substances and waste
managementmanagement• emergency planningemergency planning• emergency managementemergency management
risk analysis and managementrisk analysis and management• siting and commissioning of hazardous siting and commissioning of hazardous
installationsinstallations• operational managementoperational management• hazardous substances and waste hazardous substances and waste
managementmanagement• emergency planningemergency planning• emergency managementemergency management
K.Fedra ‘97
MC DSS Application ExampleMC DSS Application ExampleMC DSS Application ExampleMC DSS Application Example
Selecting Nuclear Power Plant Selecting Nuclear Power Plant
Sites in the Pacific Northwest Sites in the Pacific Northwest
Using Decision Analysis Using Decision Analysis
Keeney and Nair, 1977Keeney and Nair, 1977
Selecting Nuclear Power Plant Selecting Nuclear Power Plant
Sites in the Pacific Northwest Sites in the Pacific Northwest
Using Decision Analysis Using Decision Analysis
Keeney and Nair, 1977Keeney and Nair, 1977
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Problem statement:Problem statement:
identify and recommend potential identify and recommend potential
new sites suitable for a nuclearnew sites suitable for a nuclear
3,000 MWe thermal power station 3,000 MWe thermal power station in the Pacific Northwest.in the Pacific Northwest.
Problem statement:Problem statement:
identify and recommend potential identify and recommend potential
new sites suitable for a nuclearnew sites suitable for a nuclear
3,000 MWe thermal power station 3,000 MWe thermal power station in the Pacific Northwest.in the Pacific Northwest.
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Objective:Objective:
identify sites with a high probability identify sites with a high probability
for successful licensing;for successful licensing;
screen sites for detailed site screen sites for detailed site
specific studiesspecific studies
Objective:Objective:
identify sites with a high probability identify sites with a high probability
for successful licensing;for successful licensing;
screen sites for detailed site screen sites for detailed site
specific studiesspecific studies
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Two step procedure:Two step procedure:
• a screening process to identify a screening process to identify candidate sitescandidate sites
• a decision analysis to evaluate a decision analysis to evaluate and rank the candidate sitesand rank the candidate sites
Two step procedure:Two step procedure:
• a screening process to identify a screening process to identify candidate sitescandidate sites
• a decision analysis to evaluate a decision analysis to evaluate and rank the candidate sitesand rank the candidate sites
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Study area:Study area:
250,000 km250,000 km22 including the State of including the State of Washington, major river basins in Washington, major river basins in Oregon and Idaho, Oregon coast, Oregon and Idaho, Oregon coast, excluding areas around existing excluding areas around existing TPS sites.TPS sites.
Study area:Study area:
250,000 km250,000 km22 including the State of including the State of Washington, major river basins in Washington, major river basins in Oregon and Idaho, Oregon coast, Oregon and Idaho, Oregon coast, excluding areas around existing excluding areas around existing TPS sites.TPS sites.
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Hierarchy of issues:Hierarchy of issues:• safetysafety• environmentalenvironmental• socialsocial• economiceconomic
with criteria and required levels of with criteria and required levels of achievements (constraints)achievements (constraints)
Hierarchy of issues:Hierarchy of issues:• safetysafety• environmentalenvironmental• socialsocial• economiceconomic
with criteria and required levels of with criteria and required levels of achievements (constraints)achievements (constraints)
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Safety:Safety: radiation exposure radiation exposure
Distance from populated areas:Distance from populated areas:
more than 5 km from populated more than 5 km from populated places > 2,500 inhabitantsplaces > 2,500 inhabitants
more than 2 km from populated more than 2 km from populated places < 2,500 inhabitantsplaces < 2,500 inhabitants
Safety:Safety: radiation exposure radiation exposure
Distance from populated areas:Distance from populated areas:
more than 5 km from populated more than 5 km from populated places > 2,500 inhabitantsplaces > 2,500 inhabitants
more than 2 km from populated more than 2 km from populated places < 2,500 inhabitantsplaces < 2,500 inhabitants
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Safety:Safety: Flooding Flooding
Height above nearest water source:Height above nearest water source:
area must be above primary flood area must be above primary flood
plain (100 year flood)plain (100 year flood)
Safety:Safety: Flooding Flooding
Height above nearest water source:Height above nearest water source:
area must be above primary flood area must be above primary flood
plain (100 year flood)plain (100 year flood)
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Safety:Safety: Surface faulting Surface faulting
Distance from fault:Distance from fault:
area a must be more than 10 km area a must be more than 10 km
from capable or > 15 km from from capable or > 15 km from
unclassified faultsunclassified faults
Safety:Safety: Surface faulting Surface faulting
Distance from fault:Distance from fault:
area a must be more than 10 km area a must be more than 10 km
from capable or > 15 km from from capable or > 15 km from
unclassified faultsunclassified faults
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Environment:Environment: Thermal pollution Thermal pollution
Average low flow:Average low flow:
Cooling water source (river, Cooling water source (river,
reservoir) yielding 7 day average reservoir) yielding 7 day average
10 year low-flow > 5 m10 year low-flow > 5 m33/sec/sec
Environment:Environment: Thermal pollution Thermal pollution
Average low flow:Average low flow:
Cooling water source (river, Cooling water source (river,
reservoir) yielding 7 day average reservoir) yielding 7 day average
10 year low-flow > 5 m10 year low-flow > 5 m33/sec/sec
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Environment:Environment: protected areas protected areas
Relative location:Relative location:
Location must be outside Location must be outside
designated or protected sensitive designated or protected sensitive
ecological areasecological areas
Environment:Environment: protected areas protected areas
Relative location:Relative location:
Location must be outside Location must be outside
designated or protected sensitive designated or protected sensitive
ecological areasecological areas
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Socio-economics: Socio-economics: tourism, recreationtourism, recreation
Relative location:Relative location:
Location must be outside designated Location must be outside designated
scenic and recreational areasscenic and recreational areas
Socio-economics: Socio-economics: tourism, recreationtourism, recreation
Relative location:Relative location:
Location must be outside designated Location must be outside designated
scenic and recreational areasscenic and recreational areas
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Costs: Costs: routine/emergency water supply routine/emergency water supply
Cost/reliability of water source:Cost/reliability of water source:
Cooling water source (river, reservoir) Cooling water source (river, reservoir)
yielding 7 day average 10 year low- yielding 7 day average 10 year low-
flow > 5 mflow > 5 m33/sec/sec
Costs: Costs: routine/emergency water supply routine/emergency water supply
Cost/reliability of water source:Cost/reliability of water source:
Cooling water source (river, reservoir) Cooling water source (river, reservoir)
yielding 7 day average 10 year low- yielding 7 day average 10 year low-
flow > 5 mflow > 5 m33/sec/sec
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Costs: Costs: routine/emergency water supply routine/emergency water supply
Cost of pumping water:Cost of pumping water:
Location within 15 km from nearest Location within 15 km from nearest
water supply, and less than 250 m water supply, and less than 250 m
above the water levelabove the water level
Costs: Costs: routine/emergency water supply routine/emergency water supply
Cost of pumping water:Cost of pumping water:
Location within 15 km from nearest Location within 15 km from nearest
water supply, and less than 250 m water supply, and less than 250 m
above the water levelabove the water level
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Cost: Cost: delivery of major componentsdelivery of major components
Cost of providing delivery access:Cost of providing delivery access:
Location must be within 50 km of Location must be within 50 km of
navigable waterwaysnavigable waterways
Cost: Cost: delivery of major componentsdelivery of major components
Cost of providing delivery access:Cost of providing delivery access:
Location must be within 50 km of Location must be within 50 km of
navigable waterwaysnavigable waterways
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Sensitivity:Sensitivity:
assume varying the cut-off values by assume varying the cut-off values by
a small percentage; how many a small percentage; how many
potential sites are included or potential sites are included or
excluded ?excluded ?
Sensitivity:Sensitivity:
assume varying the cut-off values by assume varying the cut-off values by
a small percentage; how many a small percentage; how many
potential sites are included or potential sites are included or
excluded ?excluded ?
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Site information: Site information: (approx. 30 attributes)(approx. 30 attributes)
• area, location, present use, ownershiparea, location, present use, ownership• quality, quantity, location of water quality, quantity, location of water • geology, topography, flooding potentialgeology, topography, flooding potential• population, vegetation, wildlifepopulation, vegetation, wildlife• access to transportation networksaccess to transportation networks• local workforce, potential socio-economic local workforce, potential socio-economic
problems during construction phase, …….problems during construction phase, …….
Site information: Site information: (approx. 30 attributes)(approx. 30 attributes)
• area, location, present use, ownershiparea, location, present use, ownership• quality, quantity, location of water quality, quantity, location of water • geology, topography, flooding potentialgeology, topography, flooding potential• population, vegetation, wildlifepopulation, vegetation, wildlife• access to transportation networksaccess to transportation networks• local workforce, potential socio-economic local workforce, potential socio-economic
problems during construction phase, …….problems during construction phase, …….
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Screening of attributes:Screening of attributes:• relative importancerelative importanceannualised capital cost of the TPS is around annualised capital cost of the TPS is around
200-300 MUS$;200-300 MUS$;
annual revenue loss from adverse effects on annual revenue loss from adverse effects on fisheries is around 0-50,000 US$fisheries is around 0-50,000 US$
ignore the fishignore the fish
Screening of attributes:Screening of attributes:• relative importancerelative importanceannualised capital cost of the TPS is around annualised capital cost of the TPS is around
200-300 MUS$;200-300 MUS$;
annual revenue loss from adverse effects on annual revenue loss from adverse effects on fisheries is around 0-50,000 US$fisheries is around 0-50,000 US$
ignore the fishignore the fish
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Screening of attributes:Screening of attributes:• site-dependent variationsite-dependent variation
even though manpower costs for even though manpower costs for operations are important, they don’t operations are important, they don’t vary significantly between sitesvary significantly between sites
ignore labor costsignore labor costs
Screening of attributes:Screening of attributes:• site-dependent variationsite-dependent variation
even though manpower costs for even though manpower costs for operations are important, they don’t operations are important, they don’t vary significantly between sitesvary significantly between sites
ignore labor costsignore labor costs
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Screening of attributes:Screening of attributes:• likelihood of occurrencelikelihood of occurrenceadverse effects on crops could amount adverse effects on crops could amount
to several million US$; the probability to several million US$; the probability of such extreme losses is near zeroof such extreme losses is near zero
ignore crop lossesignore crop losses
Screening of attributes:Screening of attributes:• likelihood of occurrencelikelihood of occurrenceadverse effects on crops could amount adverse effects on crops could amount
to several million US$; the probability to several million US$; the probability of such extreme losses is near zeroof such extreme losses is near zero
ignore crop lossesignore crop losses
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Final Objectives and Criteria:Final Objectives and Criteria:Health and SafetyHealth and Safety
XX11 = site population factor= site population factor
best: 0best: 0
worst: 0.20worst: 0.20
Final Objectives and Criteria:Final Objectives and Criteria:Health and SafetyHealth and Safety
XX11 = site population factor= site population factor
best: 0best: 0
worst: 0.20worst: 0.20
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Site Population FactorSite Population Factor(US Atomic Energy Commission)(US Atomic Energy Commission)
SUM ((SUM ((dd=1,50) P(=1,50) P(dd))dd-2-2)) SPF(L) = SPF(L) = SUM ((SUM ((dd=1,50) Q(=1,50) Q(dd))-2-2))where where dd is distance in miles, is distance in miles, PP is the population within this radius, is the population within this radius,QQ is the population in this radius at a density is the population in this radius at a density
of 1,000 people per square mileof 1,000 people per square mile
Site Population FactorSite Population Factor(US Atomic Energy Commission)(US Atomic Energy Commission)
SUM ((SUM ((dd=1,50) P(=1,50) P(dd))dd-2-2)) SPF(L) = SPF(L) = SUM ((SUM ((dd=1,50) Q(=1,50) Q(dd))-2-2))where where dd is distance in miles, is distance in miles, PP is the population within this radius, is the population within this radius,QQ is the population in this radius at a density is the population in this radius at a density
of 1,000 people per square mileof 1,000 people per square mile
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Final Objectives and Criteria:Final Objectives and Criteria:Environmental EffectsEnvironmental Effects
XX22 = loss of salmonides= loss of salmonides
best: 0best: 0
worst: 100 % of fish populationworst: 100 % of fish population
Final Objectives and Criteria:Final Objectives and Criteria:Environmental EffectsEnvironmental Effects
XX22 = loss of salmonides= loss of salmonides
best: 0best: 0
worst: 100 % of fish populationworst: 100 % of fish population
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Final Objectives and Criteria:Final Objectives and Criteria:Environmental EffectsEnvironmental Effects
XX33 = ecological impacts= ecological impacts
best: 0best: 0
worst: 8 worst: 8 (subjective ordinal scale)(subjective ordinal scale)
Final Objectives and Criteria:Final Objectives and Criteria:Environmental EffectsEnvironmental Effects
XX33 = ecological impacts= ecological impacts
best: 0best: 0
worst: 8 worst: 8 (subjective ordinal scale)(subjective ordinal scale)
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
ecological impacts:ecological impacts: loss per loss per mimi2 2 for sitefor site
0 0 agricultural or urban land, no nativeagricultural or urban land, no native ecological communities affectedecological communities affected
1 1 primarily agricultural land, no wetlands primarily agricultural land, no wetlands …………......77 mature community or 90% loss of wetlandsmature community or 90% loss of wetlands and endangered species habitatand endangered species habitat8 8 100% mature forest, virgin wetlands, or100% mature forest, virgin wetlands, or endangered species habitatsendangered species habitats
ecological impacts:ecological impacts: loss per loss per mimi2 2 for sitefor site
0 0 agricultural or urban land, no nativeagricultural or urban land, no native ecological communities affectedecological communities affected
1 1 primarily agricultural land, no wetlands primarily agricultural land, no wetlands …………......77 mature community or 90% loss of wetlandsmature community or 90% loss of wetlands and endangered species habitatand endangered species habitat8 8 100% mature forest, virgin wetlands, or100% mature forest, virgin wetlands, or endangered species habitatsendangered species habitats
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Final Objectives and Criteria:Final Objectives and Criteria:Environmental EffectsEnvironmental Effects
XX44 = length of 500 kV intertie= length of 500 kV intertie
best: 0best: 0
worst: 50 milesworst: 50 miles
Final Objectives and Criteria:Final Objectives and Criteria:Environmental EffectsEnvironmental Effects
XX44 = length of 500 kV intertie= length of 500 kV intertie
best: 0best: 0
worst: 50 milesworst: 50 miles
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Final Objectives and Criteria:Final Objectives and Criteria:Socio-Economic EffectsSocio-Economic Effects
XX55 = socio-economic impacts= socio-economic impacts
best: 0best: 0
worst: 7 worst: 7 (subjective ordinal scale)(subjective ordinal scale)
Final Objectives and Criteria:Final Objectives and Criteria:Socio-Economic EffectsSocio-Economic Effects
XX55 = socio-economic impacts= socio-economic impacts
best: 0best: 0
worst: 7 worst: 7 (subjective ordinal scale)(subjective ordinal scale)
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Final Objectives and Criteria:Final Objectives and Criteria:System CostSystem Cost
XX66 = annual differential cost (30 yr)= annual differential cost (30 yr)
best: 0best: 0
worst: 40,000,000 US$ (1985)worst: 40,000,000 US$ (1985)
Final Objectives and Criteria:Final Objectives and Criteria:System CostSystem Cost
XX66 = annual differential cost (30 yr)= annual differential cost (30 yr)
best: 0best: 0
worst: 40,000,000 US$ (1985)worst: 40,000,000 US$ (1985)
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Preference StructurePreference Structure
• determine the general preferencedetermine the general preference structurestructure
• assess the single-attribute utilityassess the single-attribute utility functionsfunctions
• evaluate the scaling constantsevaluate the scaling constants
• specify the combined utility functionspecify the combined utility function
Preference StructurePreference Structure
• determine the general preferencedetermine the general preference structurestructure
• assess the single-attribute utilityassess the single-attribute utility functionsfunctions
• evaluate the scaling constantsevaluate the scaling constants
• specify the combined utility functionspecify the combined utility function
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
General Preference StructureGeneral Preference StructureIndependence of attributes:Independence of attributes:
{X{Xii, X, Xjj} } are are preferentially independentpreferentially independent
if the preference order for (xif the preference order for (x i, i, xxjj) does not) does not
depend on the levels of other attributes.depend on the levels of other attributes.
General Preference StructureGeneral Preference StructureIndependence of attributes:Independence of attributes:
{X{Xii, X, Xjj} } are are preferentially independentpreferentially independent
if the preference order for (xif the preference order for (x i, i, xxjj) does not) does not
depend on the levels of other attributes.depend on the levels of other attributes.
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Multiattribute utility functionMultiattribute utility functionattribute independenceattribute independencesuggests an suggests an
additive utility function:additive utility function:
u(x) = SUMu(x) = SUM(1,6)(1,6) ((kkiiuuii(x(xii))))where u is scaled 0 to 1, uwhere u is scaled 0 to 1, u ii are the single are the single
attribute utility functions, and kattribute utility functions, and k ii are are
scaling constants with 0<kscaling constants with 0<k ii<1<1
Multiattribute utility functionMultiattribute utility functionattribute independenceattribute independencesuggests an suggests an
additive utility function:additive utility function:
u(x) = SUMu(x) = SUM(1,6)(1,6) ((kkiiuuii(x(xii))))where u is scaled 0 to 1, uwhere u is scaled 0 to 1, u ii are the single are the single
attribute utility functions, and kattribute utility functions, and k ii are are
scaling constants with 0<kscaling constants with 0<k ii<1<1
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Single attribute utility functionsSingle attribute utility functions• 50-50 lottery method 50-50 lottery method (Keeney and Raiffa, 1976)(Keeney and Raiffa, 1976)
XX66 (cost, 0-40 M) (cost, 0-40 M)
offer various values of Xoffer various values of X66 against a against a50/50 “lottery” of 0 or 40 M.50/50 “lottery” of 0 or 40 M.Point of indifference: 22 MPoint of indifference: 22 Mu(0) = 1, and u(40) = 0 u(22) = 0.5u(0) = 1, and u(40) = 0 u(22) = 0.5
Single attribute utility functionsSingle attribute utility functions• 50-50 lottery method 50-50 lottery method (Keeney and Raiffa, 1976)(Keeney and Raiffa, 1976)
XX66 (cost, 0-40 M) (cost, 0-40 M)
offer various values of Xoffer various values of X66 against a against a50/50 “lottery” of 0 or 40 M.50/50 “lottery” of 0 or 40 M.Point of indifference: 22 MPoint of indifference: 22 Mu(0) = 1, and u(40) = 0 u(22) = 0.5u(0) = 1, and u(40) = 0 u(22) = 0.5
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Single attribute utility functionsSingle attribute utility functions
XX66 cost cost
u(40) = 0.0u(40) = 0.0
u(26) = 0.5u(26) = 0.5
u(0) = 1.0u(0) = 1.0
Single attribute utility functionsSingle attribute utility functions
XX66 cost cost
u(40) = 0.0u(40) = 0.0
u(26) = 0.5u(26) = 0.5
u(0) = 1.0u(0) = 1.0
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Single attribute utility functionsSingle attribute utility functions
XX33 ecology ecology
u(8) = 0.0u(8) = 0.0
u(5) = 0.5u(5) = 0.5
u(0) = 1.0u(0) = 1.0
Single attribute utility functionsSingle attribute utility functions
XX33 ecology ecology
u(8) = 0.0u(8) = 0.0
u(5) = 0.5u(5) = 0.5
u(0) = 1.0u(0) = 1.0
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Scaling constantsScaling constants
• ranking of attribute (importance)ranking of attribute (importance)• quantifying the kquantifying the kii
Ranking: Ranking: everything else being equal, everything else being equal, which attribute would you prefer to be which attribute would you prefer to be at its best value ?at its best value ?
kk6 6 > k> k11 > k > k22 > k > k44 > k > k55 > k > k33
Scaling constantsScaling constants
• ranking of attribute (importance)ranking of attribute (importance)• quantifying the kquantifying the kii
Ranking: Ranking: everything else being equal, everything else being equal, which attribute would you prefer to be which attribute would you prefer to be at its best value ?at its best value ?
kk6 6 > k> k11 > k > k22 > k > k44 > k > k55 > k > k33
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Scaling constantsScaling constants• quantifying the kquantifying the kii trade-off between attributes:trade-off between attributes:
Site A: SPF = 0.0 cost = 40Site A: SPF = 0.0 cost = 40Site B: SPF = 0.2 cost = Site B: SPF = 0.2 cost = ??At which cost are A and B considered At which cost are A and B considered
equivalent (indifference) ?equivalent (indifference) ?
Scaling constantsScaling constants• quantifying the kquantifying the kii trade-off between attributes:trade-off between attributes:
Site A: SPF = 0.0 cost = 40Site A: SPF = 0.0 cost = 40Site B: SPF = 0.2 cost = Site B: SPF = 0.2 cost = ??At which cost are A and B considered At which cost are A and B considered
equivalent (indifference) ?equivalent (indifference) ?
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite SelectionScaling constants Scaling constants trade-off betweentrade-off between attributes:attributes: cost cost versusversus
site factorsite factor
40 M ~ 0.040 M ~ 0.0 5 M ~ 0.25 M ~ 0.2
Scaling constants Scaling constants trade-off betweentrade-off between attributes:attributes: cost cost versusversus
site factorsite factor
40 M ~ 0.040 M ~ 0.0 5 M ~ 0.25 M ~ 0.2
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Utility functionUtility function establish probability (weight) establish probability (weight) pp such that such that option option AA: cost = 0, everything else : cost = 0, everything else
at the worst level is indifferent toat the worst level is indifferent to option option BB:: all attributes at best level (all attributes at best level (pp)) all attributes at worst level (all attributes at worst level (1-1-pp))
Utility functionUtility function establish probability (weight) establish probability (weight) pp such that such that option option AA: cost = 0, everything else : cost = 0, everything else
at the worst level is indifferent toat the worst level is indifferent to option option BB:: all attributes at best level (all attributes at best level (pp)) all attributes at worst level (all attributes at worst level (1-1-pp))
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Utility functionUtility function
p p = 0.4 = 0.4 utility of option utility of option AA::
pp(1.0)+(1-(1.0)+(1-pp)(0.0) = )(0.0) = pp
kk66 = = pp = 0.4 = 0.4
from trade-offs against kfrom trade-offs against k66, all other k, all other k ii can can
be determinedbe determined
Utility functionUtility function
p p = 0.4 = 0.4 utility of option utility of option AA::
pp(1.0)+(1-(1.0)+(1-pp)(0.0) = )(0.0) = pp
kk66 = = pp = 0.4 = 0.4
from trade-offs against kfrom trade-offs against k66, all other k, all other k ii can can
be determinedbe determined
K.Fedra ‘97
Site SelectionSite SelectionSite SelectionSite Selection
Utility functionUtility function
given all kgiven all kii, the multiattribute utility , the multiattribute utility
function can now be determined:function can now be determined:
u(x) = SUMu(x) = SUM(1,6)(1,6) ((kkiiuuii(x(xii))))
which leads to a ranking ofwhich leads to a ranking ofthe candidate sites.the candidate sites.
Utility functionUtility function
given all kgiven all kii, the multiattribute utility , the multiattribute utility
function can now be determined:function can now be determined:
u(x) = SUMu(x) = SUM(1,6)(1,6) ((kkiiuuii(x(xii))))
which leads to a ranking ofwhich leads to a ranking ofthe candidate sites.the candidate sites.