1. © K.Fedra 2007 1 DSS for Integrated Water Resources Management (IWRM) DSS methods and tools DDr. Kurt Fedra ESS GmbH, Austria kurt@ess.co.at http://www.ess.co.at Environmental Software & Services A-2352 Gumpoldskirchen DDr. Kurt Fedra ESS GmbH, Austria kurt@ess.co.at http://www.ess.co.at Environmental Software & Services A-2352 Gumpoldskirchen

2. © K.Fedra 2007 2 Workshop objectives Show the potential of DSS for IWRM;Show the potential of DSS for IWRM; Create awareness of the possibilities;Create awareness of the possibilities; Facilitate understanding by describing:Facilitate understanding by describing: –Basic concepts, theory and approaches, components, methods, the tools, the language; –Scope and benefits of possible applications, prototypical application examples; –Limitations, uncertainty, data requirements, infrastructure and institutional requirements. Get the participants interested in active participation, including the exploratory application of the on-line tools for any specific problem or project. Show the potential of DSS for IWRM;Show the potential of DSS for IWRM; Create awareness of the possibilities;Create awareness of the possibilities; Facilitate understanding by describing:Facilitate understanding by describing: –Basic concepts, theory and approaches, components, methods, the tools, the language; –Scope and benefits of possible applications, prototypical application examples; –Limitations, uncertainty, data requirements, infrastructure and institutional requirements. Get the participants interested in active participation, including the exploratory application of the on-line tools for any specific problem or project.

3. © K.Fedra 2007 3 Main topics: DSS software tools and methods, overview and comparison of basic methods Classical decision problems, decision support and optimization, scenario development DSS software tools and methods, overview and comparison of basic methods Classical decision problems, decision support and optimization, scenario development

4. © K.Fedra 2007 4 DSS tools and methods DSS: 254,000,000 hits in Google. The term DSS is frequently used for (software) systems that are only marginally related to DSS; Any SPREADSHEET is not a DSS Any DATA BASE is not a DSS Any MODELis not a DSS Any GIS is not a DSS DSS: 254,000,000 hits in Google. The term DSS is frequently used for (software) systems that are only marginally related to DSS; Any SPREADSHEET is not a DSS Any DATA BASE is not a DSS Any MODELis not a DSS Any GIS is not a DSS

5. © K.Fedra 2007 5 What is a DSS ? A Decision Support System (DSS) is a A Decision Support System (DSS) is a computer based problem solving systemcomputer based problem solving system (HW, SW, data, people) that can assist non-trivial choiceassist non-trivial choice between alternatives inbetween alternatives in complex and controversial domains.complex and controversial domains. A DSS must manage together: Set of alternatives (design)Set of alternatives (design) Preference structure (selection)Preference structure (selection)

6. © K.Fedra 2007 6 DSS tools and methods DSS should at least explicitly address: Alternatives (manage, design) Preference structure (criteria, objectives, constraints, ranking and selection rules) DSS should at least explicitly address: Alternatives (manage, design) Preference structure (criteria, objectives, constraints, ranking and selection rules)

7. © K.Fedra 2007 7 Design of alternatives Predefined set (externally defined) Expert assessment, ad hoc Expert assessment, checklists (EIA, SIA) Expert systems (rule-based) Simulation modelling, scenario analysis Mathematical programming ( optimization) Predefined set (externally defined) Expert assessment, ad hoc Expert assessment, checklists (EIA, SIA) Expert systems (rule-based) Simulation modelling, scenario analysis Mathematical programming ( optimization)

8. © K.Fedra 2007 8 Design of alternatives Predefined set (externally defined) Expert assessment, ad hoc Advantage: simple, cost efficient Limitations: arbitrary, subjective, possibly unstructured, no consistency or optimality guaranteed Predefined set (externally defined) Expert assessment, ad hoc Advantage: simple, cost efficient Limitations: arbitrary, subjective, possibly unstructured, no consistency or optimality guaranteed

9. © K.Fedra 2007 9 Design of alternatives Expert assessment, checklists (EIA, SIA) better structured than ad-hoc methods, commonly used for EIA Advantage: simple, efficient, cheap Limitations: subjective, no guarantee of completeness or consistency, no convergence to optimality Expert assessment, checklists (EIA, SIA) better structured than ad-hoc methods, commonly used for EIA Advantage: simple, efficient, cheap Limitations: subjective, no guarantee of completeness or consistency, no convergence to optimality

10. © K.Fedra 2007 10 Design of alternatives Expert assessment, checklists (EIA, SIA) Expert systems (rule-based) Advantage: very flexible, can cover qualitative and quantitative concepts, easy to use Limitations: subjective element, effort in preparing a domain specific knowledge base Expert assessment, checklists (EIA, SIA) Expert systems (rule-based) Advantage: very flexible, can cover qualitative and quantitative concepts, easy to use Limitations: subjective element, effort in preparing a domain specific knowledge base

11. © K.Fedra 2007 11 Expert system Uses first order logic for the description and assessment of alternatives: IF [ condition: variable|operator|value ] AND/OR [ condition ] test THEN [ conclusion: variable|operator|value] assignment Intelligent checklists: alternatives are generated by systematically varying input/control conditions (antecedents) Uses first order logic for the description and assessment of alternatives: IF [ condition: variable|operator|value ] AND/OR [ condition ] test THEN [ conclusion: variable|operator|value] assignment Intelligent checklists: alternatives are generated by systematically varying input/control conditions (antecedents)

12. © K.Fedra 2007 12 rankingranking

13. © K.Fedra 2007 13 Design of alternatives Simulation modelling, scenario analysis Advantage: most powerful, flexible, and versatile; high level of detail arbitrary resolution and coverage (dynamic, distributed), large body of experience and available tools Limitations: efforts and costs, data requirements (GIGO) Simulation modelling, scenario analysis Advantage: most powerful, flexible, and versatile; high level of detail arbitrary resolution and coverage (dynamic, distributed), large body of experience and available tools Limitations: efforts and costs, data requirements (GIGO)

14. © K.Fedra 2007 14 Design of alternatives Mathematical programming ( optimization) Advantage: most powerful paradigm, only one to truly design and generate alternatives to directly address objectives (goal oriented) Limitations: same as modelling, effort and data, simplifying assumptions for many methods (LP). Mathematical programming ( optimization) Advantage: most powerful paradigm, only one to truly design and generate alternatives to directly address objectives (goal oriented) Limitations: same as modelling, effort and data, simplifying assumptions for many methods (LP).

15. © K.Fedra 2007 15 Mathematical programming Maximize f(x): x in X, g(x) <= 0, h(x) = 0 where: X is a subset of R^n X is in the domain of the real-valued functions, f, g and h. The relations, g(x) <= 0 and h(x) = 0 are called constraints, f is called the objective function. Maximize f(x): x in X, g(x) <= 0, h(x) = 0 where: X is a subset of R^n X is in the domain of the real-valued functions, f, g and h. The relations, g(x) <= 0 and h(x) = 0 are called constraints, f is called the objective function.

16. © K.Fedra 2007 16 DSS tools and methods DSS should at least explicitly address: Alternatives (manage, design) Preference structure (criteria, objectives, constraints, ranking and selection rules) DSS should at least explicitly address: Alternatives (manage, design) Preference structure (criteria, objectives, constraints, ranking and selection rules)

17. © K.Fedra 2007 17 DSS tools and methods For a GIVEN set of alternatives: Primary approach to selection is RANKING, sorting Simple with single or integrated criteria (everything monetized) Difficult with multiple criteria For a GIVEN set of alternatives: Primary approach to selection is RANKING, sorting Simple with single or integrated criteria (everything monetized) Difficult with multiple criteria

18. © K.Fedra 2007 18 Cost-benefit analysis Simple and single decision criterion: Does the project (alternative ) create NET BENEFIT ? Evaluate and sum all costs Evaluate and sum all benefits Benefit – Costs > 0.0 ? Simple and single decision criterion: Does the project (alternative ) create NET BENEFIT ? Evaluate and sum all costs Evaluate and sum all benefits Benefit – Costs > 0.0 ?

19. © K.Fedra 2007 19 Cost-benefit analysis Establish net present value of net benefits (PVNB):

20. © K.Fedra 2007 20 Cost-benefit analysis where: B incremental benefit in sector i C capital and operating costs D “dis-benefits”, external and opportunity costs i sectoral index t time, r interest rate where: B incremental benefit in sector i C capital and operating costs D “dis-benefits”, external and opportunity costs i sectoral index t time, r interest rate

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22. © K.Fedra 2007 22 Decision matrix (Pugh method) Alternative Criterion (min) Case 150,000 Case 245,000 Case 355,000

23. © K.Fedra 2007 23 Decision matrix (Pugh method) AlternativesCriterion 1 (min) Criterion 2 (max) Case 150,00010 Case 2 45,000 12 Case 355,000 15

24. © K.Fedra 2007 24 Decision matrix (Pugh method) AltCrit.1 (min) W1Crit.2 (max) W2SUM ??? C1 50,000 0.01101000 10,500 C2 45,000 0.01121000 12,450 C3 55,000 0.01151000 15,550

25. © K.Fedra 2007 25 Decision matrix (Pugh method) AltCrit.1 (min) W1Crit.2 (min 20-x) W2SUM (min) C1 50,000 0.1101000 15,000 C2 45,000 0.181000 12,500 C3 55,000 0.151000 10,500

26. © K.Fedra 2007 26 Decision matrix Advantage: Simple, helps to organize data, can use any standard spreadsheet Limitations: Weights are completely arbitrary One weight per criterion is needed Issues of SCALING (needs some normalization to make criteria commensurate) Limited complexity (alternatives*criteria) Advantage: Simple, helps to organize data, can use any standard spreadsheet Limitations: Weights are completely arbitrary One weight per criterion is needed Issues of SCALING (needs some normalization to make criteria commensurate) Limited complexity (alternatives*criteria)

27. © K.Fedra 2007 27 DSS tools: preference structures Analytic Hierarchy Process (AHP) Multi-Attribute Global Inference of Quality (MAGIQ) Goal Programming ELECTRE (Outranking) PROMETHÉE (Outranking) The Evidential Reasoning Approach Analytic Hierarchy Process (AHP) Multi-Attribute Global Inference of Quality (MAGIQ) Goal Programming ELECTRE (Outranking) PROMETHÉE (Outranking) The Evidential Reasoning Approach

28. © K.Fedra 2007 28 DSS tools and methods Analytic Hierarchy Process (AHP): Based on the pairwise comparison (preference ranking) of alternatives for each of the criteria; Advantage: widely used, simple steps, group decision making oriented Limitations: does not guarantee internal consistency (cyclic dominance), rather impractical for larger sets of alternatives and criteria: 50 alternatives and 10 criteria: 1,225 comparisons ! Analytic Hierarchy Process (AHP): Based on the pairwise comparison (preference ranking) of alternatives for each of the criteria; Advantage: widely used, simple steps, group decision making oriented Limitations: does not guarantee internal consistency (cyclic dominance), rather impractical for larger sets of alternatives and criteria: 50 alternatives and 10 criteria: 1,225 comparisons !

29. © K.Fedra 2007 29 DSS tools and methods Multi-Attribute Global Inference of Quality (MAGIQ): related to AHP and Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) technique based on criteria order/ranking Advantage: nice name Limitations: similar to AHP (limited complexity), no guaranteed consistency, scaling problems. Multi-Attribute Global Inference of Quality (MAGIQ): related to AHP and Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) technique based on criteria order/ranking Advantage: nice name Limitations: similar to AHP (limited complexity), no guaranteed consistency, scaling problems.

30. © K.Fedra 2007 30 DSS tools and methods Goal Programming: a variation of linear programming to handle multiple, normally conflicting objective measures, minimizes deviation from a set of targets (goals). Advantage: based on mathematical programming related to satisficing Limitations: based on mathematical programming (assuming gradients), linear assumptions, scaling of criteria or goals. Goal Programming: a variation of linear programming to handle multiple, normally conflicting objective measures, minimizes deviation from a set of targets (goals). Advantage: based on mathematical programming related to satisficing Limitations: based on mathematical programming (assuming gradients), linear assumptions, scaling of criteria or goals.

31. © K.Fedra 2007 31 DSS tools and methods ELECTRE (Outranking) based on the pairwise comparison and “outranking” of alternatives. Outranking relation: A1 is at least as good as A2 with respect to a major subset of the criteria A1 is not too bad relative to A2 with respect to the remaining criteria ELECTRE (Outranking) based on the pairwise comparison and “outranking” of alternatives. Outranking relation: A1 is at least as good as A2 with respect to a major subset of the criteria A1 is not too bad relative to A2 with respect to the remaining criteria

32. © K.Fedra 2007 32 DSS tools and methods PROMETHÉE (Outranking) Preference Ranking Organisation METHod for Enrichment Evaluations Same old pairwise comparison ….. Advantage: nice PC based tool: (Decision Lab 2000) Limitations: as above. PROMETHÉE (Outranking) Preference Ranking Organisation METHod for Enrichment Evaluations Same old pairwise comparison ….. Advantage: nice PC based tool: (Decision Lab 2000) Limitations: as above.

33. © K.Fedra 2007 33 DSS tools and methods The Evidential Reasoning Approach Decision matrix application; combines quantitative and qualitative criteria, emphasis on perception and believes (belief decision matrix). Advantages: very flexible Limitations: very subjective The Evidential Reasoning Approach Decision matrix application; combines quantitative and qualitative criteria, emphasis on perception and believes (belief decision matrix). Advantages: very flexible Limitations: very subjective

34. © K.Fedra 2007 34 DSS tools and methods Reference point Uses a normalization of criteria between NADIR and UTOPIA, values are expressed as % achievement along that distance; implicit weights through reference (default: UTOPIA); efficient solution: closest to REF. Advantage: conceptually clean, minimal assumptions, very efficient for large data sets Limitations: assumes independent criteria (test !) non-intuitive for high dimensionality Reference point Uses a normalization of criteria between NADIR and UTOPIA, values are expressed as % achievement along that distance; implicit weights through reference (default: UTOPIA); efficient solution: closest to REF. Advantage: conceptually clean, minimal assumptions, very efficient for large data sets Limitations: assumes independent criteria (test !) non-intuitive for high dimensionality

35. © K.Fedra 2007 35 DSS tools and methods Occam’s razor: entia non sunt multiplicanda praeter necessitatem (lex parsimoniae, William of Ockham, 14 th century philosopher monk) One should not increase, beyond what is necessary, the number of entities (assumptions, parameters) required to explain anything, All things being equal, the simplest solution tends to be the right one. KISS (keep it simple, stupid) ! Occam’s razor: entia non sunt multiplicanda praeter necessitatem (lex parsimoniae, William of Ockham, 14 th century philosopher monk) One should not increase, beyond what is necessary, the number of entities (assumptions, parameters) required to explain anything, All things being equal, the simplest solution tends to be the right one. KISS (keep it simple, stupid) !

36. © K.Fedra 2007 36 DSS tools and methods

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38. © K.Fedra 2007 38 DSS tools and methods

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40. © K.Fedra 2007 40 Nile DST example Nile basin specific configuration/application Scenario analysis with embedded data bases (geo-referenced) Geo-referenced data base Monitorong stations, reservoirs/lakes Hydrological model (rainfall-runoff Agricultural model (CROPWAT) Basin management: long-term flow simulation incl. reservoirs and hydropower Nile basin specific configuration/application Scenario analysis with embedded data bases (geo-referenced) Geo-referenced data base Monitorong stations, reservoirs/lakes Hydrological model (rainfall-runoff Agricultural model (CROPWAT) Basin management: long-term flow simulation incl. reservoirs and hydropower

41. © K.Fedra 2007 41 Nile DST example Advantages: Nile basin specific application and configuration Extensive geo-referenced data base PC based application Limitations: NOT a DSS Difficult to configure (network, scenarios) Advantages: Nile basin specific application and configuration Extensive geo-referenced data base PC based application Limitations: NOT a DSS Difficult to configure (network, scenarios)

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