DSS for Integrated
Water Resources
Management (IWRM)
DSS methods and tools
DDr. Kurt Fedra
[email protected]
ESS GmbH, Austria
http://www.ess.co.at
Environmental Software & Services A-2352 Gumpoldskirchen
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Workshop objectives
• Show the potential of DSS for IWRM;
• Create awareness of the possibilities;
• 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.
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Main topics:
•
DSS software tools and methods,
overview and comparison of basic
methods
Classical decision problems,
decision support and optimization,
scenario development
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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 MODEL
is not a DSS
Any GIS
is not a DSS
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What is a DSS ?
A Decision Support System (DSS) is a
• computer based
problem solving system
(HW, SW, data, people) that can
• assist non-trivial choice
• between alternatives in
• complex and controversial domains.
A DSS must manage together:
• Set of alternatives
(design)
• Preference structure (selection)
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DSS tools and methods
DSS should at least explicitly address:
• Alternatives (manage, design)
• Preference structure (criteria,
objectives, constraints, ranking
and selection rules)
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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)
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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
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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
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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
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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)
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ranking
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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)
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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).
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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.
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DSS tools and methods
DSS should at least explicitly address:
• Alternatives (manage, design)
• Preference structure
(criteria, objectives, constraints,
ranking and selection rules)
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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
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Cost-benefit analysis
Simple and single decision criterion:
Does the project (alternative ) create
•
•
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NET BENEFIT ?
Evaluate and sum all costs
Evaluate and sum all benefits
Benefit – Costs > 0.0 ?
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Cost-benefit analysis
Establish net present value of
net benefits (PVNB):
PVNB 
  (B ) 
 C 
 D 






 (1  r ) 
 (1  r ) 
 (1  r ) 
i
t
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it
jt
it
t
t
t
t
t
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Cost-benefit analysis
PVNB 
  (B ) 
 C



 (1  r ) 
 (1  r )
i
t
where:
•
•
•
•
•
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it
it
t
t

 D 
    (1  r ) 



it
t
t
t
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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Decision matrix (Pugh method)
Case 1
Criterion
(min)
50,000
Case 2
45,000
Case 3
55,000
Alternative
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Decision matrix (Pugh method)
Alternatives
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Case 1
Criterion 1
(min)
50,000
Criterion 2
(max)
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Case 2
45,000
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Case 3
55,000
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Decision matrix (Pugh method)
Alt
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Crit.1
(min)
W1
C1
Crit.2 W2 SUM
(max)
???
50,000 0.01
10
1000 10,500
C2
45,000
0.01
12
1000 12,450
C3
55,000
0.01
15
1000 15,550
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Decision matrix (Pugh method)
Alt
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Crit.1
(min)
W1
Crit.2
C1
50,000
0.1
10
1000 15,000
C2
45,000
0.1
8
1000 12,500
C3
55,000
0.1
5
1000 10,500
(min
20-x)
W2
SUM
(min)
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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)
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DSS tools: preference structures
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•
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•
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Analytic Hierarchy Process (AHP)
Multi-Attribute Global Inference of
Quality (MAGIQ)
Goal Programming
ELECTRE (Outranking)
PROMETHÉE (Outranking)
The Evidential Reasoning Approach
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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 !
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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.
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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.
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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
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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.
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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
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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
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DSS tools and methods
Occam’s razor: entia non sunt multiplicanda
praeter necessitatem (lex parsimoniae, William of
Ockham, 14th 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.
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KISS (keep it simple, stupid) !
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DSS tools and methods
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DSS tools and methods
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DSS tools and methods
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DSS tools and methods
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Nile DST example
Nile basin specific configuration/application
Scenario analysis with embedded data
bases (geo-referenced)
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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
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Nile DST example
Advantages:
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Nile basin specific application and
configuration
Extensive geo-referenced data base
PC based application
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Limitations:
• NOT a DSS
• Difficult to configure (network,
scenarios)
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