Decision support for natural resources management:
Models, GIS and expert systems.
AI Applications, 9/3 (1995) pp 3-19.
EIA for Water Resources Development Projects
The development of large-scale water resources projects such as dams
and reservoirs, as well as irrigation schemes, or flood control
is increasingly faced by opposition on environmental or socio-economic
grounds. Environmental impact assessment (EIA) is a required component of
almost all such projects, and it is a typical example of a complex problem
involving difficult assessment and trade-offs among a diverse group of
To support a screening level assessment at an early stage of project
planning offers the possibility to introduce environmental and social
concerns early on before opposite views become entrenched and all the more
difficult to reconcile, but it also implies that very little data may
be available initially.
The MEXSES system
described below (Fedra et al., 1991)
has been implemented as a rule-based expert system, using hierarchical
checklists to perform screening level environmental impact assessment.
The current prototype system, developed for the Lower Mekong,
is geared toward the assessment
of water resources development projects such as dams and reservoirs,
hydropower and irrigation schemes, flood control, navigation,
The structure of the assessment process is based on the Asian Development
Bank's Environmental Guideline Series (ADB, 1988).
The indicators used to assess a given project are based on checklists
of items specific to a project type, covering environmental as well as
selected socio-economic topics, each indicator being rated on a
qualitative scale, from not significant to major.
In the current prototype a system of hierarchical checklists is used
with a rule-based deduction process including a recursive explain function
and a knowledge base browser, both connected to a hypertext
system. The browser and explanation function
display the near natural language rules; hypertext links them
to a handbook style definition and explanation of the terms and concepts
used in the system as well as general background information on
environmental impact assessment, including numerous examples,
to provide a tutorial framework for the assessment.
Selecting a project from a list (or from the map of the integrated GIS)
retrieves data already available on a specific project or
The Environmental Checklists are organized by project types, and grouped
into problem classes.
They include problems due to location, planning and design
problems, problems during the construction phase, problems during project
operation, and finally, environmental enhancement measures, which
looks at possible enhancement or mitigation strategies.
Project types include reservoirs and dams, hydropower projects
including transmission lines, irrigation projects, fisheries and
aquaculture, and could also include
infrastructure projects (roads and highways, sewerage, water supply,
etc.), navigation, erosion control, etc.
The checklists are designed to guide the analyst through a reasonably
complete set of expected environmental impacts for a given project type.
Subproblems or basic indicators covered in the checklists include,
for example for a reservoir project, environmental impacts from,
or in terms of:
resettlement; watershed degradation; encroachment upon precious
encroachment on historical/cultural values;
watershed erosion; reservoir siltation; impairment of navigation;
changes in groundwater hydrology, water logging;
seepage and evaporation losses; migration of valuable fish species;
inundation of mineral resources/forests;
other inundation losses and adverse effects;
earthquake hazards, and local climatic change.
To provide an assessment for each item in the checklists,
analysts can choose/set a value and then ask the system to check
their hypothesis. This triggers a backward chaining inference
system that will attempt to establish all the necessary preconditions for
the result (the hypothesis) specified.
If some of the required facts are unknown,
the inference procedure will ask the user the necessary questions.
As a final result, the user's assessment will either be confirmed or
Alternatively, the analyst can start a forward-chaining inference
procedure, where the system will reason from the available data
to arrive at a classification of impacts.
Again, missing information will have to be supplied by the
analyst in a question--answer dialogue.
The answers the user provides to the various questions posed are taken
from a menu of possible answers offered by the system from its knowledge
base. Most descriptors or variables used can be symbolic as well as
numeric, and the user can choose the appropriate format depending on the
information at hand; defaults associated with the various symbolic labels
are offered, and an additional layer of context-sensitive help, explaining
the various terms and concepts, as well as the background for each
question, the range of possible answers, and illustrative examples
are provided in the graphical interface through hypertext.
By using information that is likely to be available at an early project
stage, the system will attempt to determine the expected severity of a
given subproblem such as, eg., watershed erosion, by using rules that,
for example, consider climatic and topographic data, soil and slope
conditions, vegetation cover and land use, management practices, etc.
MEXSES uses a straight-forward knowledge representation,
combining an object oriented design for the descriptors,
the basic elements in the inference procedure,
with near natural language rules (Fedra et al, 1991; Fedra 1992).
As the basic object of the system, descriptors are the concepts or terms
used in the knowledge representation; they are linked through the rules,
that allow to derive values for a given descriptor from other descriptor
values through the standard logical operations of first order logic.
Descriptors are defined as part of the knowledge base of the system.
The definition includes basic characteristics such as name, type, and
units of measurement, and a list or range of legal values the descriptor
can take. Depending on the descriptor, these values can be symbolic,
numeric, or both.
Descriptor objects also know about methods they can use to establish
their values in a given context.
These methods include questions to ask of the user
data base or GIS references that trigger the appropriate retrieval or
estimation function and rules.
For example, data from meteorological records or flow data can be
retrieved from the nearest appropriate station, or interpolated
where necessary; values for elevation, land use, soil type, slopes,
or population density can be retrieved from the respective topical maps.
In addition to rules and simple algebraic expressions and formulas that
can be expressed within the rule syntax, methods can also
be references to complex numerical functions and entire
simulation models that can be used to obtain an appropriate value.
In the interactive dialogue, the user can choose between different
methods; priorities of methods, ie., which one should be tried
first, are also defined in the descriptor definitions and can be
dynamically modified through rules.
Finally, descriptors can have alternative sets of (partial) definitions
to be used depending on the context and under rule control.
Rules can result in the absolute assignment of descriptor values,
their relative, incremental modification, or they can be used to
control the inference strategy depending on context.