A Knowledge-Based Systems Approach to Design of Spatial Decision Support Systems for Environmental Management

A Knowledge-Based Systems Approach to Designof Spatial Decision Support Systemsfor Environmental ManagementXUAN ZHU*Department of GeographyUniversity College CorkCork, Ireland

RICHARD G. HEALEYDepartment of GeographyUniversity of PortsmouthPortsmouth PO1 3HE, UK

RICHARD J. ASPINALLGIS and Remote Sensing UnitMacaulay Land Use Research InstituteAberdeen AB9 2QJ, UK

ABSTRACT / This paper describes a framework for design-ing spatial decision support systems for environmental man-agement using a knowledge-based systems approach. Anarchitecture for knowledge-based spatial decision support

systems (KBSDSS) is presented that integrates knowledge-based systems with geographical information systems (GIS)and other problem-solving techniques. A method based onspatial influence diagrams is developed for representation ofenvironmental problems. The spatial influence diagram pro-vides an interface through which knowledge-based systemstechniques can be applied to build capabilities for problemformulation, automated design, and execution of a solutionprocess. In addition to the flexibility and developmental ad-vantages of knowledge-based systems, the KBSDSS incor-porates expert knowledge to provide assistance for structur-ing spatial influence diagrams and executing a solutionprocess that automatically integrates the GIS, data base,knowledge base, and different types of models. The frame-work is illustrated with a system, known as the Islay LandUse Decision Support System (ILUDSS), designed to assistplanners in strategic planning of land use for the develop-ment of the island of Islay, off the west coast of Scotland.

The use of computer-based systems in support ofdecision making in environmental management hasincreased dramatically over the past decade. Geographi-cal information systems (GIS), knowledge-based sys-tems (or expert systems), and other problem-solvingtechniques have been used to help environmentalscientists understand and control complex biologicaland physical systems. At the same time, there has been agreat deal of research on the development of spatialdecision support systems (SDSS). An SDSS aims toprovide a decision-making environment that incorpo-rates the data storage, computation, and analysis capa-bilities of modern computers to support decision mak-ers in making decisions effectively for environmentalmanagement.

The design and implementation of SDSS has pre-sented many challenges (Abel and others 1994). Atraditional approach to the design of SDSS is to definearchitectures that assemble a set of components, includ-

ing data-base management systems, spatial modeling,analytical models, graphical display, and tabular report-ing (Armstrong and others 1986). Given the spatialnature of environmental management problems, manySDSS have been designed and implemented using GIStechnology, coupled with specific analytical modelingtechniques and models (for examples, see Walker andMoore 1988, Pearson and others 1991, van der Vlugt1989, Negahban and others 1993). These approachesput emphasis on information access and display and onnumeric computation by analytic models.

However, the problem-solving tasks in environmen-tal management require integration, interpretation,and delivery of different representations of knowledge,including heuristic knowledge of human experts, andanalytic modeling results. For example, the resolutionof rural land use conflicts within a region involves afundamental trade-off between the physical suitabilityof the land for a given purpose and the environmentalimpacts of certain land-use patterns. There may benumerous suitability models addressing variations ofthis trade-off. However, other aspects may need to beconsidered. The interests of the local community mayrestrict new land-use development. Economic apprais-als of the different forms of land use may play a role.

KEY WORDS: Geographical information systems; Spatial decisionsupport systems; Knowledge-based systems; Spatialinfluence diagrams; Environmental management

*Author to whom correspondence should be addressed.

Environmental Management Vol. 22, No. 1, pp. 35–48 r 1998 Springer-Verlag New York Inc.

Government planning policies will also enter the deci-sion. Human judgements would be necessary in makingthis trade-off. Therefore, formal models must be inte-grated with human expertise in arriving at a finalsolution. An SDSS should be able to ‘‘use substantialknowledge about the problem domain, and shouldguide the user in systematically examining variouspotential scenarios before arriving at a decision’’ (Arm-strong and others 1990). The traditional SDSS do notfully capture the knowledge and expertise required forsolving environmental problems. The emergence ofknowledge-based systems presents an opportunity toovercome this shortcoming.

Knowledge-based systems store and interpret theknowledge and experience of a human expert, orsometimes several experts, in a specific area of interest(Giarratano and Riley 1989). They combine the abilityto simulate the heuristic reasoning of experts with anexplanation facility for justifying their reasoning andconclusions. Work on the integration of knowledge-based systems technology into the SDSS framework hasappeared in the literature (Armstrong and others 1990,Armstrong and Densham 1990, Loh and Rykiel 1992,Djokic 1993, Cowen and Ehler 1994, Diamond andWright 1988, Coulson and others 1989). Such anintegration enables the incorporation of specializedknowledge and expertise into the decision process andadds the capability of heuristic reasoning to the function-ality of the SDSS. These systems are known as knowledge-based spatial decision support systems (KBSDSS).

A very common approach to the design of KBSDSShas been through integration of existing GIS, expertsystems, and modeling techniques as components (Abeland others 1994, Djokic 1993, Zhu 1995). The effortshave been focused on making the software systemscommunicate among each other and with the data baseto facilitate ease of use and access (e.g., Djokic 1993,Loh and Rykiel 1992). However, the knowledge to helpthe user in structuring problems and in selectingappropriate models, data, and modeling strategies isnot an integral part of such systems. The user has to beaware when, how, and in what sequences to use themodels and data in combination to solve specific prob-lems, although he or she is not required to know aboutcomplex data structures and other technical details.

Densham and Goodchild (1989) argue that an SDSS‘‘should incorporate knowledge used by expert analyststo guide the formulation of the problem, the articula-tion of the desired characteristics of the solution andthe design and execution of a solution process.’’ Cowenand Shirley (1991) also suggest that a good SDSS should

make its software tools accessible to users with differentlevels of technical expertise. So far, little work has beendone to achieve these goals.

The objective of this research is to develop a newapproach to the design of SDSS within an integratedframework of GIS, expert systems, and other problem-solving techniques. A more powerful design of SDSS isintroduced using knowledge-based systems technology.The goal is to develop a KBSDSS that can incorporateexpert knowledge to provide assistance for formulationof the problem, design, and execution of a solutionprocess that automatically integrates different types ofmodels and data. A KBSDSS for rural land-use planningis presented as an example.

Overview of Our Approach

The design and use of decision support systemsdepends on domain and tools knowledge (King 1990).Domain knowledge here refers to the theories andconcepts about the problem domain for which thesystem is to be used. The focus of SDSS has been on theability to perform analyses and modeling with data andmodels. However, the decision process using informa-tion technology involves more than data interpretation.Before data and results are obtained, planners ormanagers are faced with a series of tasks: first, buildingthe data base relations and models; then decidingmodeling strategies, selecting appropriate data sets,choosing sequences of commands for analyses; andfinally, displaying the results of the analyses or offeringsolutions to the problems. Few of these tasks involvedomain knowledge of a particular environmental appli-cation. Instead, they involve knowledge of how toperform spatial modeling and how to use a set of toolsin combination for particular analytical purposes. Thistype of knowledge is called tools knowledge.

If an SDSS is to be of use in the task of problemanalysis and decision support, it should incorporatetools knowledge and expertise to help the user inselecting and integrating appropriate models and dataand in selecting suitable solution strategies to solveparticular problems (Zhu and Healey 1992, Usery andothers 1988, Lein 1992, Armstrong and others 1986).Our approach involves the development of a knowledgebase to incorporate both domain and tools knowledgeto guide the formulation of problems and provideguidance in employing different types of models anddata to solve the problems.

Environmental problems are often complex and

X. Zhu and others36

characterized by a large number of interrelated uncer-tain quantities and alternatives. Solution of the problemis possible only after it is structured and a suitableproblem model formulated. Thus, an appropriate tech-nique for problem structuring and representation isneeded to facilitate the computer representation andmanipulation of problem models. In this light, wedevelop a spatial influence diagram-based representa-tion scheme and mechanisms for formulation, represen-tation, and evaluation of problem models. A spatialinfluence diagram is a graphical representation of anenvironmental problem, which consists of nodes anddirected arcs with no cycles. The nodes represent theproblem parameters or variables relevant to a particularproblem. The arcs represent relations between thevariables. The representation scheme provides an inter-face through which knowledge-based systems tech-niques can be applied to build the capabilities forintegrating data and models logically and driving thesolution process.

An evolutionary approach to system development isan important feature of knowledge-based systems, espe-cially rule-based expert systems techniques. That is, theknowledge base can be incrementally improved byadding or modifying rules. This advantage facilitatesadvanced prototyping. In this research, we develop

methods for formulating a problem and designing anddriving the solution process in a rule-based manner.

A KBSDSS integrates capabilities for spatial, quantita-tive, and qualitative modeling. It seems unlikely thatmonolithic approaches to the development of KBSDSSwill be successful or that the results will be sustainable.Thus, our approach is to build KBSDSS within anintegrated framework of existing GIS, knowledge-basedsystems, and other problem-solving techniques. Theextensive knowledge base helps users in the appropriateuse of the GIS, knowledge-based system, and othermodeling and analysis techniques. This enables the userto exploit the power of GIS, analytical, and rule-basedmodeling in a relatively simple and fast way.

A KBSDSS Architecture

Our approach falls in the realm of knowledge-basedsystems design. An architecture for KBSDSS is pro-posed, as shown in Figure 1. The query processingsubsystem accepts users’ queries, displays results ofmodeling, and provides explanations and on-line help.It allows the user to retrieve data from the data basetogether with data derived during the modeling pro-

Figure 1. A KBSDSS architecture (from Zhu and others 1996).

Spatial Decision Support Systems 37

cess, and display them in different forms, such as maps,images, or tables. It also allows the user to retrievemeta-information about data and models.

The modeling subsystem is designed to help the userin developing useful models. The system acquires rel-evant knowledge through the knowledge acquisitionmodule. This includes meta-data for new data sets,meta-information about new models, or structures forthe problems defined by the user.

The automatic modeling module in the modelingsubsystem can formulate a problem model for a particu-lar environmental problem, within the scope permittedby the classes of problems inherent to its design. Afterthe user requests solutions to a problem, the systemelicits the preferences from him or her as to how it is tobe solved, and then incorporates these features in theformulation of a problem model. This process is calledautomatic modeling. A detailed discussion of a spatialinfluence diagram-based mechanism for problem formu-lation will be presented in a later section. After aproblem model is formulated, the system allows theuser to modify the model. This is termed user-assistedmodeling and also includes the creation of problemmodels by the user.

The problem processor plays a central role. It per-forms two main functions: inference and control. Itaccepts commands translated from the request andactions issued by the user through the query processingsubsystem and modeling subsystem; executes thesecommands; controls access to the data base, knowledgebase, and modeling tools; executes models; retrievesknowledge from the knowledge base; and makes infer-ences. The problem processor has two main inferencemechanisms: (1) designing and driving the solutionprocess for the problem, and (2) controlling the execu-tion of relevant models and utility programs. Utilityprograms are used during a solution process for conver-sion of data structures, display of spatial data and so on.

After a problem model is created through the model-ling subsystem, it is presented to the problem processor.The first mechanism of the problem processor will thenbe activated to design a solution strategy based on theproblem model and drive the solution process to derivea solution to the problem. During the process, thesecond mechanism will be activated to invoke data-basecalls and other pertinent knowledge and assign param-eter values to the models designated within the problemmodel. The second mechanism also controls the selec-tion of appropriate utility programs where necessary,schedules the models and utility programs, and makessure that they are executed in a proper sequence.

The knowledge base contains five distinct parts, eachcalled a knowledge base module (Figure 1). These arethe domain module, the meta-data module, the modelmodule, the utility program module, and the processmodule. Meta-data is knowledge about data. Model andutility program knowledge concerns the effective use ofthe models and utility programs available in the system.Process knowledge refers to knowledge that providesuser support during the problem-solving process. Gen-erally, the process of problem solving using a KBSDSSincludes formulating a problem model, evaluating themodel, and generating a solution. Process knowledge isused to guide the actual successful execution of thesesteps, direct the dialogue between the user and thesystem, and provide help messages in the course ofconsultation.

The back-end subsystem consists of the software toolsfor implementing different types of models and utilityprograms defined in the system. It contains threeseparate modules: an expert system, a GIS, and acollection of analytical procedures and utility programs.The architecture places no restrictions upon the num-ber of software tools in the back-end subsystem. Differ-ent software systems, such as statistical packages, can beadded if required to meet the needs of the problem-solving task. When new software tools are added, themeta-information about the models and the utilityprograms they support can be added to the systemthrough the knowledge acquisition module in themodeling subsystem or by modifying the model andutility program knowledge in the knowledge base.

The query processing subsystem, modeling sub-system, problem processor, and the knowledge base arebuilt within an expert system environment. The inter-face to the back-end subsystem provides the link be-tween the expert system environment and the back-endsubsystem.

Spatial Influence Diagram Methodfor Representation of Problem Models

The previous discussions indicate that a problemmodel must be built before the problem is solved.Building a problem model can be described as theprocess of structuring and simplifying a problem, andpresenting the problem parameters or elements andtheir relationships in a comprehensible way.

Graphs are usually employed in problem representa-tion and structuring. Influence diagrams are well-known graphical representations of problem models fordecision analysis (Holtzman 1989). They are representa-tions of uncertain variables and decisions that explicitly

X. Zhu and others38

reveal probablistic dependence and the flow of informa-tion (Howard and Matheson 1983). An influence dia-gram is an acyclic graph with three types of nodes andtwo types of arcs. The three types of nodes are decision,chance, and value nodes. Decision nodes represent thechoices or alternatives available to the decision maker.Chance nodes represent uncertain or probabilistic vari-ables that are not controllable by the decision maker.The value node represents the objective (or utility) tobe maximized. Arcs directed toward the value node andchance nodes are conditional and denote probabilisticdependence. Arcs pointing to decision nodes are infor-mational and indicate available information.

In this research, we extend the influence diagramidea to develop a spatial influence diagram method forrepresentation of problem models for environmentalproblems. We focus on the representation of the relation-ships among environmental variables without concernfor decisions and on computation of the expected valuein the problem. Spatial influence diagrams take intoaccount the spatial characteristics of problem variablesand their relationships. Thus, they can be seen as spatialanalogs of influence diagrams, but they are determinis-tic cases of influence diagrams without decision nodes.

Definition 1. Spatial Influence Diagram

A spatial influence diagram is a connected, acyclic,and directed graph, G 5 (N, A), with a node set N andan arc set A. The set N contains three types of nodes,partitioned into three subsets V, C, and B. There is atmost one value node v [ V, representing the expectedoutcome in solving the environmental problem. Thereare zero or more chance nodes in the set C, represent-ing environmental variables, which influence (directlyor indirectly) the value node and enable the computa-tion of an outcome of the value node. The border nodesin the set B represent environmental variables thatcorrespond to available or acquirable data. That is, thevariables denoted as border nodes already have valuesor their values can be acquired from the decisionmakers. The presence of an arc indicates that the valuesof one node are influenced by or dependent on thevalues of the other.

Figure 2 shows a spatial influence diagram con-structed as a problem model for identifying suitablesites for housing development in a certain area. Todifferentiate the three types of nodes visually, the valuenode is drawn as a rounded rectangle, chance nodes aredrawn as circles and border nodes are drawn as ellipses.

The decision maker’s expected outcome in thisland-use problem is the sites for housing development,which are directly influenced by the proximity to lakesand roads, slope, and aspect. The proximity to roads is

determined by the existing road network and weightedby intervening slopes. Slope and aspect are dependenton variations of elevation on the ground.

Definition 2. Predecessor

The predecessors of node i are the set of nodesP(i) 5 5 j [ N: ( j, i) [ A6 with arcs directly connectedfrom j to i. For example, the value node in Figure 2 hasfour predecessors. The border nodes are the set ofnodes without predecessors.

Definition 3. Successor

The successors of node i are the set of nodes S(i) 5

5 j [ N: (i, j) [ A6 with arcs directly connected from i toj. For example, in Figure 2, ‘‘Elevation’’ has two succes-sors. The value node has no successors.

The nodes in a spatial influence diagram can beviewed from two levels. First, the labeled nodes and arcsrepresent the structural level showing the relationshipsof the problem elements diagrammatically. The secondlevel is the functional level, which consists of specifica-tions for all nodes and their relationships. Each node atthis level is represented by the following seven catego-

Figure 2. A spatial influence diagram.


ries of information: (1) the name of the node, (2) thetype of the node (value, chance or border), (3) itspredecessors, (4) its successors, (5) a functional modelrepresenting the attribute relation between the nodeand its predecessors, (6) a GIS model representing thespatial relation between the node and its predecessors,and (7) the associated data representing its values.

Here, functional models can be analytical models(logic relations, mathematical equations, or conditionalprobability distributions) or rule-based models (if-thenrules). GIS models are constructed using GIS analysisfunctions (e.g., neighborhood functions). The values ofnodes may be represented as attribute data only or asmaps with different thematic values.

The information relating to a node is stored as anode frame. Suppose SuitabilityForHousing is a rule-based model for evaluating site suitability for housingdevelopment according to the proximity to lakes androads, slope, and aspect; overlay is a GIS model forcombining several data layers. Figure 3 shows a framerepresentation of the value node ‘‘Sites for HousingDevelopment.’’ Because it is a value node, it has nosuccessors. Before it is evaluated, the node has noassociated data.

The structural level provides a basis for the designand execution of a solution process. The functionallevel determines the models and data to be used in thesolution process to derive the values or outcomes ofeach node.

Problem Formulation

The process by which a real problem is formulatedplays a crucial role in automated modeling using aKBSDSS. The formulation of a problem is indeed theformulation of a spatial influence diagram. The processinvolves elicitation of preferences and an extensivesearch both of the existing domain variables in thedomain knowledge base module and of the availabledata described in the meta-data knowledge base mod-ule.

In the knowledge base, there are two types of domain

knowledge: knowledge about domain variables andknowledge about preferences. Each domain variable isrepresented by a set of information, including thevariables on which its values are dependent (or itspredecessors), an analytical model or rule-based modelrepresenting its attribute relation with these variables,and a GIS model representing their spatial relation.Similar to that of a node, the representation of adomain variable is a logical representation of its relation-ships with other domain variables. A domain variablemay correspond to an individual node in a spatialinfluence diagram (including the value node).

Preference knowledge expresses the preferences fora particular problem-solving approach that decisionmakers might have within a certain decision-makingcontext. It is used to build an objective model for aparticular problem to derive a solution. Each objectivemodel accounts for a set of attributes, which aredenoted by the predecessors of the value node in aspatial influence diagram. They are formed by indicat-ing those attributes that are relevant to the problem inhand. For example, one user may specify the fourattributes to be considered in selecting housing develop-ment sites as shown in Figure 2. Then, an objectivemodel will be formed accounting for the proximity tolakes, proximity to roads, slope, and aspect. Anotheruser may only be concerned about the proximity toroads and slope. In this case, another objective modelcan be formed accounting for only the two attributes.Preference knowledge is represented as rules, and it isused interactively with the decision maker to form anobjective model for a specific problem based on his orher choice of relevant information to use.

Suppose a user has specified an expected outcome ofproblem solving. The system first checks the domainknowledge module. If there is no domain variablecorresponding to the expected outcome, the system willreport to the user that the problem cannot be solvedusing the existing knowledge stored in the system.Otherwise, the system checks the meta-data knowledgemodule to see whether the expected outcome is avail-able in the data base. If it is, the process terminates. Thesystem will present the outcome to the user. No furtherformulation process is needed. Otherwise, the systemwill set the expected outcome to be the value node.Then the system elicits from the user a set of attributesthat directly influence the value node, builds an objec-tive model accounting for these attributes, sets them tobe the predecessors of the value node, and puts theminto a predecessor set K. Afterwards, the system picks upone node from the set K. If its value is available in thedata base, is to be provided by the user, or has no

Figure 3. Frame representation of the node ‘‘Sites for Hous-ing Development.’’

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predecessors, it is set as a border node and added to thediagram. Otherwise, it is set to be a chance node, addedto the diagram, and its predecessors are searched for inthe existing domain variables and put into the set K.The process repeats itself until all border nodes arefound. After the formulation process is completed, aspatial influence diagram is formed. A procedure toformulate a spatial influence diagram in pseudo-codeterms is presented in Appendix I.

This process involves the creation of a frame represen-tation for each node (such as that shown in Figure 3).When a node is added to the diagram, the informationabout the node type, its successors and associated data isobtained through reasoning during the formulationprocess as described above, while the information aboutits predecessors and associated models is obtained fromthe corresponding domain variable. Therefore, theprocedure creates a fully specified spatial influencediagram without any user intervention (Zhu 1995).

Design and Execution of a Solution Process

The structure in the spatial influence diagram clearlyindicates a logic for obtaining the solution of theproblem it represents. The process of calculating thevalues of all nodes in their natural topological order iscalled evaluation. The set of directed arcs correspondsto an evaluation order. For example, a value of avariable i could be obtained by first evaluating a variablej, and then evaluating i based on the value of j. Thisevaluation order would correspond to a two-node spa-tial influence diagram with an arc from i to j.

A node is said to be an assessed node if it has a value.Otherwise, it is an unassessed node. Most border nodesare assessed nodes; where unassessed, the system willprompt the user for its value. Our solution procedurewill systematically remove assessed nodes from thediagram until an outcome of the value node is obtained.When a node is removed, its value is propagated to itssuccessors. When the values of all the predecessors of anode have been obtained, the node can be evaluated.

A solution procedure for a problem based on aspatial influence diagram is given in Appendix II. Thisprocedure can be used to evaluate any spatial influencediagram and drive and execute a solution process forthe problem. Every step of the procedure removes atleast one node, so it will always terminate.

Each individual node is evaluated automatically dur-ing the solution procedure. The evaluation of a nodeinvolves model scheduling if both an analytical orrule-based model and a GIS model are used, selectionof utility programs where necessary, checking of input

data, assignment of parameter values, and execution ofthe models. All of these are controlled by heuristic rulesin the knowledge base (Zhu 1995).

An Example of System Designand Implementation

A pilot KBSDSS is presented here to demonstrate theframework described above. The system, called the IslayLand Use Decision Support System (ILUDSS), wasdeveloped to support planners in assessing the poten-tial for different types of land use on the island of Islay,off the west coast of Scotland. The system can evaluateland use potential according to planners’ preferencesand assessments relating to various criteria and relatedspatial and nonspatial evaluation factors, such as physi-cal suitability, proximity to desirable and undesirableland features, and the minimum area required for eachland parcel.

ILUDSS is designed for implementation in a UNIXworkstation under the X-windows environment by inte-grating three software tools: ARC/INFO, CLIPS, andHARDY (Figure 4). The three systems operate togetherto present the user with the appearance of a singlesystem.

ARC/INFO (ESRI 1991) is a GIS software system,which is used to acquire data, build data bases, anddevelop and execute simple analytical models, GISmodels, and utility programs. INFO is a module ofARC/INFO for attribute data management. ARC/INFOalso provides the ARC Macro Language (AML) forautomating frequently performed actions and creatinguser-defined commands.

CLIPS (NASA 1993) provides an expert system envi-ronment for building the query processing subsystem,the modeling subsystem, the problem processor, andthe knowledge base. CLIPS is also used to build andmake inferences on rule bases, each representing arule-based model.

HARDY (Smart 1993) is a diagramming tool. It isused to display, create, edit, and maintain land-usemodels in the form of spatial influence diagrams. It isalso used to build a user interface. CLIPS is embeddedin HARDY.

The three systems are integrated through two links:HARDY-ARC/INFO communication and ARC/INFO-CLIPS data exchange (Zhu 1995). The underlyingcomplexities of interprocess communication are invis-ible to the user.

ILUDSS fully supports query, automatic modeling,and user-assisted modeling (Zhu and others 1996). Byautomatic modeling, the process of evaluating the


potential for a particular land use can be described bythe following steps: (1) specifying the type of land useand related evaluation factors, (2) building an objectivemodel, (3) formulating a land-use model, (4) evaluat-ing the formulated model, and (5) generating anoutput describing the land-use potential for the speci-fied land use. Below we are going to evaluate theland-use potential for afforestation using ILUDSS toillustrate this process (Figure 5).

Specifying the Type of Land Use and a Setof Attributes for Evaluation

ILUDSS allows the user to specify his or her land-useinterests and the factors to be considered in the assess-ment of the land-use potential through a dialogue. In itspresent implementation, the automatic modeling mod-ule in ILUDSS can deal with the three types of land use:farming, afforestation, and peat-cutting. The systemdisplays the list, from which the user can select one. Thesystem then gives a list of attributes to be used asevaluation factors. At present, ILUDSS takes three

classes of attributes into account: physical land suitabil-ity, proximity (to desirable and undesirable land fea-tures), and area (the minimum area required for eachland parcel). The user can choose any combination ofthe attributes for evaluating the potential for the speci-fied land use through simply clicking menu items. Inour example, the user specifies afforestation as the landuse of interest, and physical land suitability, proximity toroads, and proximity to sites of special scientific interest(SSSI) as the attributes being considered in the evalua-tion. Therefore, the system determines that the ex-pected outcome of problem solving is ‘‘potential sitesfor afforestation.’’

Building an Objective Model

After the user has selected the type of land use and aset of attributes for evaluation, ILUDSS builds anobjective model using rules in the domain knowledgebase module, which accounts for the selected type ofland use and the attributes (Zhu 1995). In our example,the system builds an objective model called forestsite1,

Figure 4. Overall Structure of ILUDSS.

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containing lines of AML, which combines data aboutthe physical land suitability for forestry, the proximity toroads, and the proximity to SSSI, and identifies thepotential areas for afforestation by including the landphysically suited to forestry and within a certain dis-tance (to be specified by the user) to roads, butexcluding SSSI. Once an objective model has beenbuilt, the process of formulating a land-use modelbegins.

Formulating a Land-Use Model

A land-use model is represented as a spatial influ-ence diagram. The formulation of a land use modelfollows the formulation mechanism described in Appen-dix I. In our example, at the beginning, the system setsthe expected outcome ‘‘Potential Sites for Afforesta-tion’’ as the value node, the attributes ‘‘Physical LandSuitability for Forestry,’’ ‘‘Proximity to Roads,’’ and‘‘Proximity to SSSI Areas’’ as the predecessors of thevalue node, and puts them into a predecessor set. Thus,an initial spatial influence diagram is formed. Then, thesystem picks up one variable from the predecessor set,

for example ‘‘Physical Land Suitability for Forestry.’’The system first checks the meta-data knowledge basemodule and finds that there are no data (value) aboutit. Therefore, the system proceeds to look for itspredecessors in the domain knowledge base moduleand put them into the predecessor set. Here, ‘‘Alti-tude,’’ ‘‘Land Capability for Agriculture,’’ and ‘‘LandCapability for Forestry’’ are its predecessors, and areadded to the initial spatial influence diagram and a newspatial influence diagram is formed. After that, thesystem picks up another variable from the predecessorset, for example ‘‘Proximity to Roads,’’ and repeats theprocess above. The node ‘‘Proximity to Roads’’ isexpanded with ‘‘Roads.’’ Next, the node ‘‘Proximity toSSSI Areas’’ is expanded with the node ‘‘Sites of SpecificScientific Interest.’’ Afterwards, the system looks for thevalues for ‘‘Altitude,’’ ‘‘Land Capability for Agriculture,’’‘‘Land Capability for Forestry,’’ ‘‘Roads’’ and ‘‘Sites ofSpecific Scientific Interest’’ in succession. They all exist inthe data base. Thus, the system sets them to be bordernodes. Finally, a complete spatial influence diagram isobtained and presented graphically to the user (Figure 6).

Figure 5. Screenshot of ILUDSS.


As the formulation process does not produce anyquestion, it is invisible to the user.

Evaluating the Land-Use Model

After a land-use model has been formulated andpresented in a diagram window, evaluation is activatedby clicking the Evaluation menu on the menu bar in thediagram window (Figure 5). The evaluation process ischaracterized by a dialogue with the user. Part of thedialogue during the consultation is presented in Appen-dix III. In such a dialogue, the system asks the user toinput data when required, informs the user about thestatus of the operations, and gives explanations andother helpful messages where necessary. The wholeprocess is controlled by the solution procedure de-scribed in Appendix II. The system is responsible for theoverall flow of the evaluation, i.e., it determines theorder in which each node in the spatial influencediagram is evaluated and triggers parameterization andexecution of models and utility programs and interpre-tation of results.

Evaluation starts from the border nodes. In ourexample, initially, there are five border nodes in thespatial influence diagram (Figure 6). Since their valuesare stored in the data base, the system first transferstheir values to each of their successors and then deletesthem from the diagram. After the five border nodes areremoved, ‘‘Physical Land Suitability for Forestry,’’ ‘‘Prox-imity to Roads,’’ and ‘‘Proximity to SSSI Areas’’ can be

evaluated. The system first evaluates ‘‘Physical LandSuitability for Forestry’’ using a rule-based model forest-suit and a GIS model overlay based on the data of‘‘Altitude,’’ ‘‘Land Capability for Agriculture,’’ and‘‘Land Capability for Forestry.’’ Some utility programsare used during the evaluation for converting INFOdata to CLIPS facts and vice versa (here, overlay isexecuted by ARC/INFO and forestsuit is executed byCLIPS). The operations of utility programs are notreported to the user. The system automatically assignsappropriate values to the required arguments of themodels and selected utility programs, executes themodel overlay first, and then the model forestsuit.

After the value of ‘‘Physical Land Suitability forForestry’’ is obtained, it becomes an assessed node, andits value can be displayed in the map display window ifthe user requests. Then the system propagates its valueto the value node, removes it from the diagram, andproceeds to evaluate ‘‘Proximity to Roads.’’ The value of‘‘Proximity to Roads’’ is derived from the data for‘‘Roads’’ using the GIS model buffering. The system firstassigns the values to its arguments. One of the argu-ments of buffering is the buffer distance, which has to bespecified by the user. Thus, the system prompts the userto input his or her preferred distance to roads. After thevalue has passed to the model, the system activates itand creates a buffer zone around the roads. Theevaluation of ‘‘Proximity to SSSI Areas’’ has a similarprocess. The value node ‘‘Potential Sites for Afforesta-tion’’ is evaluated when all its predecessors acquirevalues. The value of the value node is derived by theobjective model forestsite1 built during the formulationof the land-use model. The result of the execution offorestsite1 is the outcome required by the user. The finalresult is interpreted using a map and presented to theuser in the map display window (Figure 7). Thiscompletes the session run.

Conclusions

In this paper, we have presented a knowledge-basedsystems approach for SDSS design. A pilot KBSDSS hasbeen developed using the approach. The approachpresented offers several advantages, including the abil-ity to structure and formulate problems, to automatethe solution process, and to provide support for inte-grated use of GIS, rule-based, and other modelingtechniques.

The spatial influence diagram is an abstraction ofenvironmental problems on which knowledge-basedsystems techniques have been applied to build capabili-

Figure 6. A spatial influence diagram constructed for evaluat-ing the land-use potential for afforestation.

X. Zhu and others44

ties for problem formulation, automated design, andexecution of a solution process. It also serves as a logicalmodel, within which GIS, analytical, and rule-basedmodels are integrated logically to solve a particularproblem.

The automatic modeling module in a KBSDSS canformulate a spatial influence diagram based on theuser’s expected outcome for a particular problem andhis or her preferences over its solution. Automaticmodeling does not require users to have knowledgeabout models, data, and their usage. The user-assistedmodeling module allows the user to construct a spatialinfluence diagram. The user may first conceptualize hisor her problem first at the structure level and then addapplicable models and data existing in the system toeach node to represent the problem at the functionallevel. User-assisted modeling requires users to haveexperience in GIS and spatial modeling and knowledgeabout the use of the models and data in the system, butthey do not need to know the implementation details of

the models and data. After a spatial influence diagramhas been formulated, the extensive knowledge basein the system helps to retrieve relevant data from thedata base, assign the parameter values to the modelsdesignated in the problem model, select suitable utilityprograms, and choose the appropriate tools to executethem. Therefore, the system can be accessible to awider range of users. Furthermore, since they containmuch of the functionality of knowledge-basedsystems, KBSDSS can be used to distribute the exper-tise of domain experts and can be developed incremen-tally.

However, the KBSDSS architecture presented heredoes not provide facilities to assimilate new knowledgethrough learning. The mechanism for problem formu-lation is not flexible enough to allow the user to easilyincorporate new elements into the problem model toaccount for aspects of the problem that were eithertotally or partially unanticipated in the system’s knowl-edge base. There remain other practical issues to be

Figure 7. Potential sites for afforestation.


explored, such as model validity, data quality, anduncertainty, which are common considerationsfor designing computer-based systems. Further researchand case studies will be necessary to address theseproblems.

KBSDSS should be built for specific domains. Focus-ing on a particular domain is essential for developingmeaningful knowledge bases. How effective theKBSDSS applications will be depends on the natureof their problem domains and on how well theapplications address the needs of individual decisionmakers. KBSDSS technology can enhance the capa-bilities of SDSS. However, since descriptions of en-vironmental problems and potential solution proce-dures are very diverse, it may be impossible to develop

an SDSS that can fully automate the problem solvingprocess.

Acknowledgments

We wish to thank Dr. Julian Smart of the ArtificialIntelligence Application Institute, University of Edin-burgh, for providing the HARDY software. The majorpart of the work was conducted in the Department ofGeography, University of Edinburgh. We are grateful tothe department for providing the necessary equipment,computing facilities, and technical support. Thanks arealso due to the Scottish Natural Heritage for supplyingthe data sets.

Appendix

Appendix I. A procedure for problemformulation.

X. Zhu and others46

Appendix II. A solution procedure for a problem.

Appendix III. A sample dialogue in a IL-UDSS session. The dialogue between thesystem and the user is through pop-up dia-logue windows. The items and buttons onthe pop-up dialogue windows are enclosedin square brackets []. Annotations are in-serted to give explanatory notes and de-scribe the user’s actions or inputs.


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A Knowledge-Based Systems Approach to Design of Spatial Decision Support Systems for Environmental Management