1 Environmental Decision Support Systems: a conceptual framework and application examples THESE présentée à la Faculté des sciences, de l’Université de

1

Environmental Decision

Support Systems: a conceptual framework and

application examples

Environmental Decision

Support Systems: a conceptual framework and

application examplesTHESETHESE

présentée à la Faculté des sciences,présentée à la Faculté des sciences,

de l’Université de Genèvede l’Université de Genève

pour obtenir le grade de Docteur ès sciences, pour obtenir le grade de Docteur ès sciences,

mention interdisciplinairemention interdisciplinairepar Kurt Fedra de Vienne (Autriche)par Kurt Fedra de Vienne (Autriche)

THESETHESE

présentée à la Faculté des sciences,présentée à la Faculté des sciences,

de l’Université de Genèvede l’Université de Genève

pour obtenir le grade de Docteur ès sciences, pour obtenir le grade de Docteur ès sciences,

mention interdisciplinairemention interdisciplinairepar Kurt Fedra de Vienne (Autriche)par Kurt Fedra de Vienne (Autriche)

2

Overview ...Overview ...

• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis• Analytical ToolsAnalytical Tools• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

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Thesis:Thesis:

lit.: the act of laying down

• a position or proposition that a person advances and offers to maintain by argument;

• a dissertation that embodies results of original research and especially substantiating a specific view.

lit.: the act of laying down

• a position or proposition that a person advances and offers to maintain by argument;

• a dissertation that embodies results of original research and especially substantiating a specific view.

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Thesis: Thesis:

Environmental problems require a new Environmental problems require a new approach to decision support and approach to decision support and decision making because:decision making because:

• it is impossible to solve the it is impossible to solve the inverse problem (HOW TO) due inverse problem (HOW TO) due to the to the complexities of complexities of environmental systemsenvironmental systems;;

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Thesis:Thesis:

• it is impossible to solve decision it is impossible to solve decision making problems unequivocally making problems unequivocally due to the due to the complexities and complexities and changing naturechanging nature of the decision of the decision making process itself.making process itself.

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Thesis:Thesis:

As a consequence, any practical As a consequence, any practical approach has to beapproach has to be

– iterativeiterative (multi tiered)(multi tiered)

– adaptiveadaptive (learning)(learning)

– interactiveinteractive (end user involvement)(end user involvement)

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ObjectivesObjectives

• To provide background and context:To provide background and context:– environmental problems, DSS processes, environmental problems, DSS processes,

regulatory frameworkregulatory framework

• To analyse available tools and their To analyse available tools and their constraints, issues of uncertaintyconstraints, issues of uncertainty

• To propose a generic conceptual To propose a generic conceptual framework exploiting modern ITframework exploiting modern IT

• To illustrate the approach with concrete To illustrate the approach with concrete application examplesapplication examples

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• IntroductionIntroduction

• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis• Analytical ToolsAnalytical Tools• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

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Environmental Systems and ProblemsEnvironmental Systems and Problems

Environmental systems areEnvironmental systems are• complexcomplex• dynamicdynamic• spatially distributedspatially distributed• highly non-linearhighly non-linear

Coupled processes on a multitude of scales,Coupled processes on a multitude of scales,

Processes not directly observableProcesses not directly observable

Effects of interventions are delayedEffects of interventions are delayed

Environmental systems areEnvironmental systems are• complexcomplex• dynamicdynamic• spatially distributedspatially distributed• highly non-linearhighly non-linear

Coupled processes on a multitude of scales,Coupled processes on a multitude of scales,

Processes not directly observableProcesses not directly observable

Effects of interventions are delayedEffects of interventions are delayed

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This makes environmental This makes environmental systems difficult to:systems difficult to:

• observeobserve• understandunderstand• predictpredict• controlcontrol

This makes environmental This makes environmental systems difficult to:systems difficult to:

• observeobserve• understandunderstand• predictpredict• controlcontrol

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• Everything is connected to everything elseEverything is connected to everything else

• Everything must go somewhereEverything must go somewhere

• Nature knows bestNature knows bestCommoner, The Closing CycleCommoner, The Closing Cycle

Basic mechanism of environmental systems:Basic mechanism of environmental systems:

(negative) feedback(negative) feedback(Malthus, Liebig, Darwin, Wiener, Meadows)(Malthus, Liebig, Darwin, Wiener, Meadows)

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Basic environmental problems: Basic environmental problems:

imbalances:imbalances:

• exhaustion of resourcesexhaustion of resourceswater, soil, biodiversity, landscapewater, soil, biodiversity, landscape

• pollution pollution (combustion products, (combustion products, synthetic chemicals, radiation, noise)synthetic chemicals, radiation, noise)

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Generic root of problems:Generic root of problems:

uncoupling of geochemical uncoupling of geochemical and ecological (but also and ecological (but also monetary) cyclesmonetary) cycles

Generic root of problems:Generic root of problems:

uncoupling of geochemical uncoupling of geochemical and ecological (but also and ecological (but also monetary) cyclesmonetary) cycles

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History of environmental degradation:History of environmental degradation:

• from ancient Greece through the from ancient Greece through the Middle Ages to the industrial Middle Ages to the industrial revolutionrevolution

• massive use of fossil fuel and massive use of fossil fuel and synthetic chemicalssynthetic chemicals

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Silent Spring, Earth Day, US NEPASilent Spring, Earth Day, US NEPA

Stockholm Conference, UNEPStockholm Conference, UNEP

EIA and national legislationEIA and national legislation

Rio and Agenda 21Rio and Agenda 21

international accords (Kyoto)international accords (Kyoto)

ISO 1400, EMAS, Responsible CareISO 1400, EMAS, Responsible Care

Silent Spring, Earth Day, US NEPASilent Spring, Earth Day, US NEPA

Stockholm Conference, UNEPStockholm Conference, UNEP

EIA and national legislationEIA and national legislation

Rio and Agenda 21Rio and Agenda 21

international accords (Kyoto)international accords (Kyoto)

ISO 1400, EMAS, Responsible CareISO 1400, EMAS, Responsible Care

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Sustainability:Sustainability:

meeting the meeting the needsneeds of the presentof the present without without compromising the compromising the needs of the futureneeds of the future..

Needs depend on Needs depend on valuesvalues..

Values are subject to Values are subject to choicechoice, , i.e., man-made i.e., man-made decisions.decisions.

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Regulatory response:Regulatory response:

• constraints on emissionsconstraints on emissions

• planning requirements (EIA)planning requirements (EIA)

• setting of standards, BATsetting of standards, BAT

• public information and participationpublic information and participation

• voluntary compliance (market)voluntary compliance (market)

Regulatory response:Regulatory response:

• constraints on emissionsconstraints on emissions

• planning requirements (EIA)planning requirements (EIA)

• setting of standards, BATsetting of standards, BAT

• public information and participationpublic information and participation

• voluntary compliance (market)voluntary compliance (market)

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Information requirements:Information requirements:

• monitoring to demonstrate compliancemonitoring to demonstrate compliance

• forecasts in assessment proceduresforecasts in assessment procedures

• public informationpublic information

Standards are arbitrary, i.e., subject to Standards are arbitrary, i.e., subject to policy level decisionspolicy level decisions

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• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems

• Applied Systems AnalysisApplied Systems Analysis• Analytical ToolsAnalytical Tools• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

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Applied Systems AnalysisApplied Systems Analysis

is designed to provide policy advise or is designed to provide policy advise or decision support. This requires:decision support. This requires:

• useful information:useful information:• reliablereliable

• timelytimely

• affordableaffordable

• an effective communication systeman effective communication system

is designed to provide policy advise or is designed to provide policy advise or decision support. This requires:decision support. This requires:

• useful information:useful information:• reliablereliable

• timelytimely

• affordableaffordable

• an effective communication systeman effective communication system

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Applied Systems AnalysisApplied Systems Analysis

• Framework: considers the larger context, Framework: considers the larger context, integration, emphasis on bounding and integration, emphasis on bounding and boundary conditions, side effects;boundary conditions, side effects;

• Tools: logical, quantitative, structured, Tools: logical, quantitative, structured, combining physical sciences with combining physical sciences with technology and socio-economic aspects: technology and socio-economic aspects: simulation/optimisationsimulation/optimisation modelsmodels;;

• UncertaintyUncertainty: an important and inherent part : an important and inherent part of the method.of the method.

• Framework: considers the larger context, Framework: considers the larger context, integration, emphasis on bounding and integration, emphasis on bounding and boundary conditions, side effects;boundary conditions, side effects;

• Tools: logical, quantitative, structured, Tools: logical, quantitative, structured, combining physical sciences with combining physical sciences with technology and socio-economic aspects: technology and socio-economic aspects: simulation/optimisationsimulation/optimisation modelsmodels;;

• UncertaintyUncertainty: an important and inherent part : an important and inherent part of the method.of the method.

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Sources of Uncertainty ...Sources of Uncertainty ...

• Data and observation uncertaintyData and observation uncertainty

• Model structure uncertaintyModel structure uncertainty

• Parameter uncertaintyParameter uncertainty

• Algorithmic uncertainty/errorAlgorithmic uncertainty/error

• Implementation errorsImplementation errors

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Uncertainty and ReliabilityUncertainty and Reliability

If a hypothesis (models are compound If a hypothesis (models are compound hypotheses) can not unambiguously be hypotheses) can not unambiguously be falsified, we leave falsified, we leave (in Poppers strict view)(in Poppers strict view) the realm of science.the realm of science.

Following pragmatic instrumentalism Following pragmatic instrumentalism (Feyerabend 1975), (Feyerabend 1975), the question the question becomes:becomes:

• how useful is the hypothesis (model)how useful is the hypothesis (model)

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Uncertainty ...Uncertainty ...

in the context of decision making:in the context of decision making:

where denote controllable and where denote controllable and uncontrollable forcings,and denotes all uncontrollable forcings,and denotes all stochastic disturbances again stochastic disturbances again (Young,1983)(Young,1983)

),,,,,( tuuxfxdt

dx dc dc uu ,

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Implications for decision makingImplications for decision making

Uncertainty analysis offers the possibility Uncertainty analysis offers the possibility to take uncertainty explicitly into to take uncertainty explicitly into account: probability of violating account: probability of violating standards, population exposure, etc: standards, population exposure, etc: – median, 95%, worst casemedian, 95%, worst case

• explicit treatment of uncertainty in risk explicit treatment of uncertainty in risk analysis: analysis: risk is the output variablerisk is the output variable

Uncertainty analysis offers the possibility Uncertainty analysis offers the possibility to take uncertainty explicitly into to take uncertainty explicitly into account: probability of violating account: probability of violating standards, population exposure, etc: standards, population exposure, etc: – median, 95%, worst casemedian, 95%, worst case

• explicit treatment of uncertainty in risk explicit treatment of uncertainty in risk analysis: analysis: risk is the output variablerisk is the output variable

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Risk AssessmentRisk Assessment

Risk contours aroundRisk contours arounda plant locationa plant location(a source of risk):(a source of risk):

1010-6-6 events/year events/year unacceptableunacceptable individual riskindividual risk

1010-8 -8 events/yearevents/year negligible risknegligible risk

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Treatment of uncertaintyTreatment of uncertainty

A Monte Carlo approach to uncertainty A Monte Carlo approach to uncertainty analysis analysis (Fedra, 1981, 1983):(Fedra, 1981, 1983):

– model structure identification by model structure identification by hypothesis testinghypothesis testing

– parameter estimationparameter estimation

– error propagation (forecasting under error propagation (forecasting under uncertainty)uncertainty)

A Monte Carlo approach to uncertainty A Monte Carlo approach to uncertainty analysis analysis (Fedra, 1981, 1983):(Fedra, 1981, 1983):

– model structure identification by model structure identification by hypothesis testinghypothesis testing

– parameter estimationparameter estimation

– error propagation (forecasting under error propagation (forecasting under uncertainty)uncertainty)

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Analysis procedure:Analysis procedure:

• For a given model structure, a For a given model structure, a parameter vector is sampled randomly parameter vector is sampled randomly from an from an a prioria priori defined parameter defined parameter space;space;

• Each model run is classified or Each model run is classified or evaluated by a set of rules;evaluated by a set of rules;

• The resulting subsets are analysed.The resulting subsets are analysed.

• For a given model structure, a For a given model structure, a parameter vector is sampled randomly parameter vector is sampled randomly from an from an a prioria priori defined parameter defined parameter space;space;

• Each model run is classified or Each model run is classified or evaluated by a set of rules;evaluated by a set of rules;

• The resulting subsets are analysed.The resulting subsets are analysed.

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The model is a vector function The model is a vector function ff with with Domain Domain D(f)D(f) (set of all possible (set of all possible parameter vectors) and Range parameter vectors) and Range R(f)R(f) (set (set of all possible behavior vectors).of all possible behavior vectors).

If If RDRD is a subset of is a subset of RR, the invers image of , the invers image of RDRD under under ff is the subset of is the subset of D(f):D(f):

The model is a vector function The model is a vector function ff with with Domain Domain D(f)D(f) (set of all possible (set of all possible parameter vectors) and Range parameter vectors) and Range R(f)R(f) (set (set of all possible behavior vectors).of all possible behavior vectors).

If If RDRD is a subset of is a subset of RR, the invers image of , the invers image of RDRD under under ff is the subset of is the subset of D(f):D(f):

RDxfxRDf )(:)(1

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This subset is denoted This subset is denoted PMPM, the set of all , the set of all parameter vectors resulting in parameter vectors resulting in acceptable model results.acceptable model results.

For identification, we define For identification, we define RDRD by a set by a set of constraint conditions (rules) derived of constraint conditions (rules) derived from the set of observations that from the set of observations that capture the expected (allowable) capture the expected (allowable) system behavior.system behavior.

This subset is denoted This subset is denoted PMPM, the set of all , the set of all parameter vectors resulting in parameter vectors resulting in acceptable model results.acceptable model results.

For identification, we define For identification, we define RDRD by a set by a set of constraint conditions (rules) derived of constraint conditions (rules) derived from the set of observations that from the set of observations that capture the expected (allowable) capture the expected (allowable) system behavior.system behavior.

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selection/classification:selection/classification:

RS’: accepted RS’: accepted RS’’: rejectedRS’’: rejected

MNRSn

RDRSRSRS

MRSn

RDRSRSRS

ii

ii

)''(

)(:''

)'(

)(:'

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Error propagation can provide an estimate Error propagation can provide an estimate of reliability over time (forecasts):of reliability over time (forecasts):

• plotting the coefficient of variation plotting the coefficient of variation against input change (decision variable) against input change (decision variable) and time:and time:– uncertainty increases with the forecasting

period (time horizon) and the input change (degree of extrapolation).

Error propagation can provide an estimate Error propagation can provide an estimate of reliability over time (forecasts):of reliability over time (forecasts):

• plotting the coefficient of variation plotting the coefficient of variation against input change (decision variable) against input change (decision variable) and time:and time:– uncertainty increases with the forecasting

period (time horizon) and the input change (degree of extrapolation).

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• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis

• Analytical Tools: models, ES, GISAnalytical Tools: models, ES, GIS• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis

• Analytical Tools: models, ES, GISAnalytical Tools: models, ES, GIS• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

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Analytical toolsAnalytical tools

To obtain, organise, analyse and To obtain, organise, analyse and communicate useful information:communicate useful information:

• models models

• expert systems expert systems

• GISGISintegrated with a multi-media user interfaceintegrated with a multi-media user interface

To obtain, organise, analyse and To obtain, organise, analyse and communicate useful information:communicate useful information:

• models models

• expert systems expert systems

• GISGISintegrated with a multi-media user interfaceintegrated with a multi-media user interface

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Environmental models:Environmental models:

Based on conservation lawsBased on conservation laws

Basic processes:Basic processes:• growth and trophic relationsgrowth and trophic relations

• chemical transformationschemical transformations

• transport and diffusiontransport and diffusion

• radiationradiation

Based on conservation lawsBased on conservation laws

Basic processes:Basic processes:• growth and trophic relationsgrowth and trophic relations

• chemical transformationschemical transformations

• transport and diffusiontransport and diffusion

• radiationradiation

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Typical models:Typical models:

• Population models (lifestock, forests)Population models (lifestock, forests)

• Flow and transport (water, air)Flow and transport (water, air)

• Photochemical models (ozone)Photochemical models (ozone)

• Noise and radiationNoise and radiation

Typical models:Typical models:

• Population models (lifestock, forests)Population models (lifestock, forests)

• Flow and transport (water, air)Flow and transport (water, air)

• Photochemical models (ozone)Photochemical models (ozone)

• Noise and radiationNoise and radiation

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Expert systems:Expert systems:use RULES and first order logic instead of use RULES and first order logic instead of

differential equationsdifferential equations

• can process symbolic datacan process symbolic data

• can represent uncertainty explicitlycan represent uncertainty explicitly

• can explain the inference procedurecan explain the inference procedure

Hybrid systems integrated with numerical Hybrid systems integrated with numerical modelsmodels

Expert systems:Expert systems:use RULES and first order logic instead of use RULES and first order logic instead of

differential equationsdifferential equations

• can process symbolic datacan process symbolic data

• can represent uncertainty explicitlycan represent uncertainty explicitly

• can explain the inference procedurecan explain the inference procedure

Hybrid systems integrated with numerical Hybrid systems integrated with numerical modelsmodels

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Expert SystemsExpert Systems

Symbolic logic:Symbolic logic:

rule-based expert systemsrule-based expert systemsIF condition operator conditionIF condition operator condition

AND ….AND ….

OROR

THEN consequenceTHEN consequence

conditions can be formulated in terms ofconditions can be formulated in terms of

symbols, ranges, distributions, fuzzy setssymbols, ranges, distributions, fuzzy sets

Symbolic logic:Symbolic logic:

rule-based expert systemsrule-based expert systemsIF condition operator conditionIF condition operator condition

AND ….AND ….

OROR

THEN consequenceTHEN consequence

conditions can be formulated in terms ofconditions can be formulated in terms of

symbols, ranges, distributions, fuzzy setssymbols, ranges, distributions, fuzzy sets

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Expert SystemsExpert Systems

IF wind_speed IF wind_speed == very_low== very_low AND radiation AND radiation == strong== strong AND time AND time == daytime== daytimeTHEN stability = very_unstableTHEN stability = very_unstable

wind_speed:wind_speed:very_lowvery_low [0.1, 1.0, 2.0][0.1, 1.0, 2.0]low low [2.0, 2.5, 3.0][2.0, 2.5, 3.0]mediummedium [3.0, 4.0, 5.0]..[3.0, 4.0, 5.0]..

IF wind_speed IF wind_speed == very_low== very_low AND radiation AND radiation == strong== strong AND time AND time == daytime== daytimeTHEN stability = very_unstableTHEN stability = very_unstable

wind_speed:wind_speed:very_lowvery_low [0.1, 1.0, 2.0][0.1, 1.0, 2.0]low low [2.0, 2.5, 3.0][2.0, 2.5, 3.0]mediummedium [3.0, 4.0, 5.0]..[3.0, 4.0, 5.0]..

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Analytical toolsAnalytical tools

Geographic information systems (GIS)Geographic information systems (GIS)

• capture capture

• manipulatemanipulate

• analyse analyse

• display display

spatial data spatial data (geometry, topology, attributes)(geometry, topology, attributes)

Integration with numerical models: Integration with numerical models: dynamics, complex analysisdynamics, complex analysis

Geographic information systems (GIS)Geographic information systems (GIS)

• capture capture

• manipulatemanipulate

• analyse analyse

• display display

spatial data spatial data (geometry, topology, attributes)(geometry, topology, attributes)

Integration with numerical models: Integration with numerical models: dynamics, complex analysisdynamics, complex analysis

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• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis• Analytical ToolsAnalytical Tools

• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

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Decision Support ParadigmsDecision Support Paradigms

Decision making means Decision making means

choice between alternatives.choice between alternatives.This requires:This requires:• problem awarenessproblem awareness• alternatives to choose fromalternatives to choose from• a preference structurea preference structure• a selection mechanisma selection mechanism

Decision making means Decision making means

choice between alternatives.choice between alternatives.This requires:This requires:• problem awarenessproblem awareness• alternatives to choose fromalternatives to choose from• a preference structurea preference structure• a selection mechanisma selection mechanism

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Decision Support ParadigmsDecision Support Paradigms

Information systems:Information systems:raising problem awarenessraising problem awareness

Scenario analysis: WHAT IFScenario analysis: WHAT IFgenerating and evaluating alternativesgenerating and evaluating alternatives

Optimisation: HOW TOOptimisation: HOW TOdesigning and selecting alternativesdesigning and selecting alternatives

Information systems:Information systems:raising problem awarenessraising problem awareness

Scenario analysis: WHAT IFScenario analysis: WHAT IFgenerating and evaluating alternativesgenerating and evaluating alternatives

Optimisation: HOW TOOptimisation: HOW TOdesigning and selecting alternativesdesigning and selecting alternatives

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Information SystemsInformation Systems

State-of-the-Environment Reporting:State-of-the-Environment Reporting:uses issues and indicators, trend analysisuses issues and indicators, trend analysis

Monitoring of complianceMonitoring of compliancecomparison with standardscomparison with standards

Raising problem awarenessRaising problem awarenessreporting, public information systemsreporting, public information systems

State-of-the-Environment Reporting:State-of-the-Environment Reporting:uses issues and indicators, trend analysisuses issues and indicators, trend analysis

Monitoring of complianceMonitoring of compliancecomparison with standardscomparison with standards

Raising problem awarenessRaising problem awarenessreporting, public information systemsreporting, public information systems

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Information SystemsInformation Systems

Monitoring of compliance:Monitoring of compliance:

early warning systemsearly warning systemstry to determine WHEN a problem try to determine WHEN a problem

will occur, the WHAT is known, will occur, the WHAT is known, the response predefined and the response predefined and automatedautomated

Monitoring of compliance:Monitoring of compliance:

early warning systemsearly warning systemstry to determine WHEN a problem try to determine WHEN a problem

will occur, the WHAT is known, will occur, the WHAT is known, the response predefined and the response predefined and automatedautomated

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Scenario AnalysisScenario Analysis

Environmental Impact Assessment (EIA)Environmental Impact Assessment (EIA)

regulates the project planning procedure:regulates the project planning procedure:• description of the baseline scenario (status quo)description of the baseline scenario (status quo)

• description of project alternativesdescription of project alternatives

• assessment of alternatives (WHAT IF)assessment of alternatives (WHAT IF)

• mitigation of likely problemsmitigation of likely problems

• communication and public participationcommunication and public participation

Environmental Impact Assessment (EIA)Environmental Impact Assessment (EIA)

regulates the project planning procedure:regulates the project planning procedure:• description of the baseline scenario (status quo)description of the baseline scenario (status quo)

• description of project alternativesdescription of project alternatives

• assessment of alternatives (WHAT IF)assessment of alternatives (WHAT IF)

• mitigation of likely problemsmitigation of likely problems

• communication and public participationcommunication and public participation

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OptimisationOptimisation

• Classical methods based on Classical methods based on gradient search gradient search (requires (requires differentiable models)differentiable models)

• Ordinal Optimisation Ordinal Optimisation (Ho, 1999)(Ho, 1999)

• Numerical Optimisation Numerical Optimisation (Monte (Monte Carlo, heuristics)Carlo, heuristics)

• Classical methods based on Classical methods based on gradient search gradient search (requires (requires differentiable models)differentiable models)

• Ordinal Optimisation Ordinal Optimisation (Ho, 1999)(Ho, 1999)

• Numerical Optimisation Numerical Optimisation (Monte (Monte Carlo, heuristics)Carlo, heuristics)

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• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis• Analytical ToolsAnalytical Tools• Decision Support ParadigmsDecision Support Paradigms

• A generic DSS frameworkA generic DSS framework• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

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A generic DSS frameworkA generic DSS framework

To represent the complexity of To represent the complexity of environmental systems and problems, and environmental systems and problems, and the reality of the decision making process, the reality of the decision making process, these elements have to be put together.these elements have to be put together.

Leads to a generic DSS approach that isLeads to a generic DSS approach that is• multi-tiered multi-tiered • iterative iterative • interactiveinteractive..

To represent the complexity of To represent the complexity of environmental systems and problems, and environmental systems and problems, and the reality of the decision making process, the reality of the decision making process, these elements have to be put together.these elements have to be put together.

Leads to a generic DSS approach that isLeads to a generic DSS approach that is• multi-tiered multi-tiered • iterative iterative • interactiveinteractive..

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background databackground data• sectoral modelssectoral models

emission scenariosemission scenarios• impact modelsimpact models

impact scenariosimpact scenarios• global modelsglobal models

policy scenariospolicy scenarios• discrete optimisationdiscrete optimisation

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1.1. Obtain a description of the Obtain a description of the input change input change (e.g., stress) resulting from some project, (e.g., stress) resulting from some project, activity, or policy, maintaining economic activity, or policy, maintaining economic efficiency and sectoral objectives and efficiency and sectoral objectives and constraintsconstraints

Example: energy system, transportation Example: energy system, transportation systemsystem

1.1. Obtain a description of the Obtain a description of the input change input change (e.g., stress) resulting from some project, (e.g., stress) resulting from some project, activity, or policy, maintaining economic activity, or policy, maintaining economic efficiency and sectoral objectives and efficiency and sectoral objectives and constraintsconstraints

Example: energy system, transportation Example: energy system, transportation systemsystem

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2.2. Obtain a representation of the Obtain a representation of the environmental systems response, e.g., environmental systems response, e.g., ambient air quality as a function of the ambient air quality as a function of the input change (emissions) over different input change (emissions) over different averaging periods (e.g., relating to air averaging periods (e.g., relating to air quality standards for hours, days, years).quality standards for hours, days, years).

Example: urban air quality modelsExample: urban air quality models

2.2. Obtain a representation of the Obtain a representation of the environmental systems response, e.g., environmental systems response, e.g., ambient air quality as a function of the ambient air quality as a function of the input change (emissions) over different input change (emissions) over different averaging periods (e.g., relating to air averaging periods (e.g., relating to air quality standards for hours, days, years).quality standards for hours, days, years).

Example: urban air quality modelsExample: urban air quality models

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3.3. Obtain spatially distributed measures of Obtain spatially distributed measures of impacts impacts such as population exposure or such as population exposure or environmental damages, or a cost environmental damages, or a cost function for different levels of stress.function for different levels of stress.

Example: popluation exposure, forest Example: popluation exposure, forest damagedamage

3.3. Obtain spatially distributed measures of Obtain spatially distributed measures of impacts impacts such as population exposure or such as population exposure or environmental damages, or a cost environmental damages, or a cost function for different levels of stress.function for different levels of stress.

Example: popluation exposure, forest Example: popluation exposure, forest damagedamage

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4.4. Minimize the distributed impact function Minimize the distributed impact function subject to economic constraints by subject to economic constraints by distributing the maximum acceptable distributing the maximum acceptable costs. Alternatively, allocate maximum costs. Alternatively, allocate maximum allowable emission levels maintaining allowable emission levels maintaining environmental standards.environmental standards.

Example: emission control optimisationExample: emission control optimisation

4.4. Minimize the distributed impact function Minimize the distributed impact function subject to economic constraints by subject to economic constraints by distributing the maximum acceptable distributing the maximum acceptable costs. Alternatively, allocate maximum costs. Alternatively, allocate maximum allowable emission levels maintaining allowable emission levels maintaining environmental standards.environmental standards.

Example: emission control optimisationExample: emission control optimisation

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5.5. Use the permissible stress or emission levels Use the permissible stress or emission levels as constraints on the initial detailed (dynamic, as constraints on the initial detailed (dynamic, spatially distributed) sectoral models.spatially distributed) sectoral models.

6.6. Repeat to obtain a number of (sectorally) Repeat to obtain a number of (sectorally) optimal feasible scenarios.optimal feasible scenarios.

7.7. Use a discrete multi-criteria approach to find an Use a discrete multi-criteria approach to find an efficient (preferred, compromise) solution (trade-efficient (preferred, compromise) solution (trade-off between sectoral aspirations) that satisfies off between sectoral aspirations) that satisfies the objectives of all actors.the objectives of all actors.

5.5. Use the permissible stress or emission levels Use the permissible stress or emission levels as constraints on the initial detailed (dynamic, as constraints on the initial detailed (dynamic, spatially distributed) sectoral models.spatially distributed) sectoral models.

6.6. Repeat to obtain a number of (sectorally) Repeat to obtain a number of (sectorally) optimal feasible scenarios.optimal feasible scenarios.

7.7. Use a discrete multi-criteria approach to find an Use a discrete multi-criteria approach to find an efficient (preferred, compromise) solution (trade-efficient (preferred, compromise) solution (trade-off between sectoral aspirations) that satisfies off between sectoral aspirations) that satisfies the objectives of all actors.the objectives of all actors.

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The basic concept:The basic concept: iteration between iteration between

levels of representationlevels of representation, ,

using the output from one level as a using the output from one level as a constraint on the more detailed constraint on the more detailed representation of another level, and the representation of another level, and the aggregate output from a detailed level as aggregate output from a detailed level as an input to a more aggregate level.an input to a more aggregate level.

Trade off Trade off coverage versus detailcoverage versus detail

The basic concept:The basic concept: iteration between iteration between

levels of representationlevels of representation, ,

using the output from one level as a using the output from one level as a constraint on the more detailed constraint on the more detailed representation of another level, and the representation of another level, and the aggregate output from a detailed level as aggregate output from a detailed level as an input to a more aggregate level.an input to a more aggregate level.

Trade off Trade off coverage versus detailcoverage versus detail

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• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis• Analytical ToolsAnalytical Tools• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework

• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

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ImplementationImplementation

Conceptually:Conceptually:

• object-oriented designobject-oriented design

Technically:Technically:

• distributed client-server systemsdistributed client-server systems

Key concept: Key concept: integrationintegration

Conceptually:Conceptually:

• object-oriented designobject-oriented design

Technically:Technically:

• distributed client-server systemsdistributed client-server systems

Key concept: Key concept: integrationintegration

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• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis• Analytical ToolsAnalytical Tools• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework• ImplementationImplementation

• Application Domains and ExamplesApplication Domains and Examples• Discussion and ConclusionsDiscussion and Conclusions

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Application Domains and ExamplesApplication Domains and Examples

• State of the Environment ReportingState of the Environment Reporting

• Early WarningEarly Warning

• Environmental Impact AssessmentEnvironmental Impact Assessment

• Risk Assessment and ManagementRisk Assessment and Management

• Integrated Decision Support Systems:Integrated Decision Support Systems:– risk managementrisk management– urban air qualityurban air quality– regional developmentregional development

• State of the Environment ReportingState of the Environment Reporting

• Early WarningEarly Warning

• Environmental Impact AssessmentEnvironmental Impact Assessment

• Risk Assessment and ManagementRisk Assessment and Management

• Integrated Decision Support Systems:Integrated Decision Support Systems:– risk managementrisk management– urban air qualityurban air quality– regional developmentregional development

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• IntroductionIntroduction• Environmental Systems and ProblemsEnvironmental Systems and Problems• Systems AnalysisSystems Analysis• Analytical ToolsAnalytical Tools• Decision Support ParadigmsDecision Support Paradigms• A generic DSS frameworkA generic DSS framework• ImplementationImplementation• Application Domains and ExamplesApplication Domains and Examples

• Discussion and ConclusionsDiscussion and Conclusions

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Discussion and ConclusionsDiscussion and Conclusions

• Environmental problems keep growingEnvironmental problems keep growing

• Environmental awareness increasesEnvironmental awareness increases

• Environmental legislation changesEnvironmental legislation changes

• Public participation in the decision making Public participation in the decision making process increasesprocess increases

Demand for environmental information and Demand for environmental information and decision support systems increasesdecision support systems increases

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Decision support systems:Decision support systems:

• data layerdata layer

• analytical toolsanalytical tools

• user interfaceuser interface

Decision support systems:Decision support systems:

• data layerdata layer

• analytical toolsanalytical tools

• user interfaceuser interface

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Data resources layer:Data resources layer:• multiple sourcesmultiple sources

• visualisation visualisation

• interactive analysisinteractive analysisto integrate the best available data and to integrate the best available data and

informationinformation

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Analytical tools:Analytical tools:

• multiple representationmultiple representation

• interactive analysisinteractive analysis

• explicit uncertaintyexplicit uncertainty

to represent multiple actorsto represent multiple actors

Analytical tools:Analytical tools:

• multiple representationmultiple representation

• interactive analysisinteractive analysis

• explicit uncertaintyexplicit uncertainty

to represent multiple actorsto represent multiple actors

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User interface:User interface:

• graphical,symbolicgraphical,symbolic

• interactive, easy to useinteractive, easy to use

• Internet accessibleInternet accessible

to support a diverse audienceto support a diverse audience

User interface:User interface:

• graphical,symbolicgraphical,symbolic

• interactive, easy to useinteractive, easy to use

• Internet accessibleInternet accessible

to support a diverse audienceto support a diverse audience

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Paradigm change:Paradigm change:• more complex problemsmore complex problems

• participatory processes, civic participatory processes, civic society, diverse audiencesociety, diverse audience

• increasing increasing demanddemand for for informationinformation

Paradigm change:Paradigm change:• more complex problemsmore complex problems

• participatory processes, civic participatory processes, civic society, diverse audiencesociety, diverse audience

• increasing increasing demanddemand for for informationinformation

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Paradigm change:Paradigm change:• information technology promises information technology promises

instantaneous and ubiquitous instantaneous and ubiquitous access to informationaccess to information

• research results are directly research results are directly accessible beyond the academic accessible beyond the academic communitycommunity

Paradigm change:Paradigm change:• information technology promises information technology promises

instantaneous and ubiquitous instantaneous and ubiquitous access to informationaccess to information

• research results are directly research results are directly accessible beyond the academic accessible beyond the academic communitycommunity

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Paradigm change:Paradigm change:• changed nature of discourse from changed nature of discourse from

scientific correctness, precision, scientific correctness, precision, verification, formal proof verification, formal proof

to political feasibility, acceptability, to political feasibility, acceptability, MehrheitsfähigkeitMehrheitsfähigkeit;;

• from abstract optimalityfrom abstract optimality

to to good enoughgood enough

Paradigm change:Paradigm change:• changed nature of discourse from changed nature of discourse from

scientific correctness, precision, scientific correctness, precision, verification, formal proof verification, formal proof

to political feasibility, acceptability, to political feasibility, acceptability, MehrheitsfähigkeitMehrheitsfähigkeit;;

• from abstract optimalityfrom abstract optimality

to to good enoughgood enough

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Paradigm change:Paradigm change:DSS do not offer optimal solutions DSS do not offer optimal solutions

(given a set of preferences)(given a set of preferences) but a but a mechanism to make the process mechanism to make the process open, accessible, and the solution open, accessible, and the solution acceptable to a majority.acceptable to a majority.

Paradigm change:Paradigm change:DSS do not offer optimal solutions DSS do not offer optimal solutions

(given a set of preferences)(given a set of preferences) but a but a mechanism to make the process mechanism to make the process open, accessible, and the solution open, accessible, and the solution acceptable to a majority.acceptable to a majority.

Documents

1 Environmental Decision Support Systems: a conceptual framework and application examples THESE présentée à la Faculté des sciences, de l’Université de