An analysis of environmental expert system applications

An Analysis of Environmental Ezpert System Applications: B. Page

An Analysis of Environmenta l Expert S y s t e m Appl icat ions * B. Page Faculty of Informatics, University of tIamburg, Schliiterstr. 70, D-2000 tIamburg 13, Federal Republic of Germany

ABSTRACT:

This study outlines the current state of expert system technology in environmental protection. The paper defines general characteristics of expert systems and identifies the kind of problems in environmental protection which are well suited for expert system use. This study introduces, classifies and briefly describes twenty- one expert system approaches from Canada and the F.R.G., many of them still in an early development state.

The analysis and evaluation show that not many expert systems are in actual use for practical environmental decision making. Finally the areas with the highest potential for successful employment of today's expert system technology are identified and a number of research needs stated. More progress is needed towards truly operational, "intelligent" ( i.e. AI- based ) environmental information systems.

Keywords: Expert system(s), knowledge based system, environmental information system, decision support system, environmental protection, environmental management.

i. INTRODUCTION

Effective protection of our environment is largely dependent on accurate information on its state and dynamics. The characteristics of environmental information typically include:

* Information from many disciplines is necessary, much of it local, ill- organized, proprietary, or value-oriented.

* Problem-solving responsib- ilities, and therefore the users of information, are

decentralized at all levels of government.

* Critical value judgements and complex tradeoffs among private and social goals are required as part of the decision-making process.

* Uncertainty and conflicting information are so common that problem resolution often requires specialized intermediaries or experts to authenticate the information or to represent the views

Paper received 15 July 1989 and in final form $ January 1980 Referees: Drs. ttqlliam R. ~[oninoer and Judith ]tL l[ushon

* This paper summarizes the content of a comparative study on environmental expert sysXems in Canada and the Federal Re- vuMie of Germany." carried out by tan autkor at t~e Alberta

An Analysis of Environmental Expert System Applications: B. Page

of one party to an issue authoritatively." (/16/,p.242)

Computers are an integral part of contemporary environmental protection information management. They are used routinely for monitoring data collection, data analysis, communication, information storage and retrieval, simulation modeling, mapping, etc. Computers are better than humans at numerical computation ("number crunching"), information storage, and the repetitive operations that these tasks entail. Advances in Artificial Intelligence (AI) techniques promise to expand our use of computers far beyond large-scale routine computation. AI is an interdisciplinary methodology for problem solving and is concerned with designing "intel ~ ligent" computer systems -- sy~ stems exhibiting characteristics we associate with intelligent hu~ man behavior /3/. In contrast to traditional computer programs, AI or knowledge-based programs func ~ tion more like human problem sol ~ vers in that they deal with sym ~ bolic, non-algorithmic methods. Conventional algorithms are step- by-step-procedures for solving problems. Many human reasoning processes, however, tend to be non-algorithmic: we do more than just follow formalized step-by- step-procedures, which are pre ~ cisely defined beforehand.

With the increasing maturity of knowledge-based techniques new dimensions in assisting users in environmental decision making are at hand.

2. EXPERT SYSTEMS FOR ENVIRONMENTAL PROTECTION

In this section, the basic characteristics of expert systems are first reviewed, followed by a discussion of the nature of prob-

tection. The main body outlines the state of environmental expert systems in Canada and the Federal Republic of Germany.

2.1 Basic Expert System Characteristics

Artificial Intelligence (AI) and in particular its subfield of expert systems that has the best record of successful applications and is consequently the area with the most immediate potential for industry and administration are highly rated topics in modern information processing.

Expert systems (XPS) are special Knowledge-Based Systems (KBS) for

modelling and using the knowledge of qualified experts for a specific, limited domain in order to solve complex problems of a particular type or support their solution.

Although the two terms KBS and XPS are used interchangeably at times, it should be noted that basically knowledge-based systems represent a more general concept in the sense that they also coin- cide with other AI subfields such as computer vision or natural language processing.

The principle characteristics of XPSs with respect to their goal, attributes and methodology are summarized in Fig. 1 (trans- lated from (/43/, p. 5). Basic- ally, there are a number of characteristics which can be considered potential advantages of an XPS, namely:

- expertise transfer,

- an apparent "intelligent" behavior in performing complex tasks,

- an ability to deal with


incomplete and inprecise knowledge and data,

- an ability to explain and justify conclusions,

- helping inexperienced users in gaining experience,

- systematic consistency in the application of knowledge,

- assistance in the process of problem and formulation solving.

Charac[erlstics of Expert Systems

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In general, expert systems can be divided into a number of problem categories, namely interpretation, diagnosis, monitoring and control design, planning, and prediction (/22/).

This common classification seems not to be optimal for the assignment of problem solving strategies, because different problem categories can be solved with the same strategy. There- fore, we follow /43/ here and identify only three main XPS problem solving types. The aim of interpretation, diagnosis, monitoring and control is mostly to recognize known patterns, i.e., to identify an object, a fault, or an alarm state. Here, a solution is selected from a set of given alternatives. In contrast, the solution in design and planning consists of small building blocks to be assembled together (e.g. configuration of computer systems). There aretoo many combinations to allow for a selection from a complete set of configuration alternatives. These building blocks are actions in planning and components of the object to be designed in design problems, which is, however, less important for the general problem solving strategy. In this way we have problems in expert systems of the types diagnoses (i.e., classification, interpretation) and construction (i.e., configuration, design, planning). A third problem solving class in expert systems are prediction (fore ~ casting) systems, where no given goals are to be met but only the consequences of actions or events are to be predicted or simulated. Into ,this group we can also clas ~ sify training and instruction systems for knowledge, transfer.

2.2 Nature of Problem Solving in Environmental Protection

Environmental protection is a multidisciplinary area and requires specialized expers in such diverse fields as chemistry, biology, statistics, engineering, law, and this is rarely found in one person. Beyond that, know-


ledge on the environment is sub- ject to rapid growth and change. This is due to intensified research and development activities in environmental technology and to the rapid increase in environmental legislation. An important motivation for decison makers for employing expert system technolo ~ gy in environmental planning is the preparation of data and in ~ formation in a consistent and complete manner.

Environmental knowledge is not only of empirical but also of heuristic nature ("rules of thumb"). Many times, no complete analytical or algorithmic description of the main influence parameters or actions and reac- tions can be identified. Further- more, knowledge is often vague and incomplete.

Environmental management is ty ~ pically organized in a decentra ~ lized manner. Operational deci ~ sions in environmental protection are usually made by local admini ~ strations on a community level. In this local setting, expert knowledge of the multidiscipli~ nary type mentioned above is rarely at hand. Expert systems providing this knowledge could be a very useful tool in local environmental decision making.

Expert systems are well suited for storing knowledge of different kinds and origin and for providing this information for problem solving. The representation of heuristic knowledge as well as the processing of vague and incomplete knowledge is well understood in expert system technology. Modification of an expert knowledge base is usually easier to handle than adapting algorithmic problem, solving programs to new findings.

Expert system technology could also provide useful assistance in such important problem areas as user orientation for large and manifold environmental data bases

vironmental literature, research projects) as well as in running complex simulation models.

2.3 State of Environmental Expert Systems in Canada and the Federal Republic of Germany

In 1986, D. Waterman /58/ assembled a list of 181 projects using an expert system approach. He classified these into 16 application fields such as manufact~ uring, medicine, meteorology, chemistry, law. The environmental XPS category is noticeably absent. Reasons for this omission could be that environmental ex~ pert systems requireexpert knowledge from more than one domain (i.e., multidisciplinary problem areas, see above), and that rules in environmental planning and management have not been explic- itly described (see /27/).

Nonetheless, expert system technology is starting to be applied in environmental problem solving. In 1987, J. M. Hushon referenced more than 20 environmental. XPSs,developed in th U.S., in her review article "Expert systems for environmental problems" /27/. Since then, several other review articles (/47/,/37/, /14/,/42/,/40/), conference publ- ications (/36/, /35/, /i/), and research reports (/19/) have appeared and include references to a growing body of work in this area, some of which is being conducted in Canada and Germany. In this section of the paper, 21 XPS approaches in environmental protection from Ca- nada and the Federal Republic of Germany will be introduced. They will be classified with respect to the categories defined above (diagnosis /planning/prediction) and some of them will briefly outlined. A summary table as well as an evaluation based on an exchange with developers of about ~i~ ~ "~nv~ronmental the XPSs

An Analysis of Environmental Ezperl System Applications: B. Page

under consideration and on literature review are given in section 3.

2.3.1 Diagnosis/Interpretation Systems

XPSs for diagnosis and interpretation comprise the largest area of environmental expert system development. Typical environmental applications in this class include the assessment of environmental impacts of projects, the identification of ecological species (plants, animals), and the interpretation of environmental laws and regulations. On a higher level of complexity are systems dealing with the evaluation of contaminated sites and substances, as well as systems to provide expert assistance in emergency situations.

a) Environmental Assessment

Expert systems for environmental assessment, particularly those concerned with screening of environmental impacts, involve the concept of diagnosis and are one of the most successful application areas of expert systems. Government agencies are required to assess most new pro ~ jects for potential environmental effects. However, the expertise needed for assessing potential impacts or the efficacy of miti- gations often cannot be obtained locally.

SCREENER - An Expert System for Environmental Assessment. - Environmental and Social Systems Analysts Ltd., Vancouver, British Columbia, Canada, /12/, /i/).

The "SCREENER" system was designed to provide agencies with an automated decision support tool to aid people with relatively little environmental assess ~ ment expertise in makinQ basic

assessments of the potential im ~ pacts of projects, and in do ~ cumenting the basis for their assessments. It implements a sub- set of the screening categories, developed in 1986 for initial assessments of proposed projects, by the Canadian Federal Environ ~ mental Assessment Review Office (FEARO) .

The developers stress that "the intent of the systems is not to make final environmental assessments of projects, but to sep- arate or 'screen' out those projects requiring more detailed analysis from those which can proceed with little or no modification. For those projects requiring further assessments, SCREE ~ NER can be used to generate a list of potential interactions between project activities and the particular components of the environment at risk. This list can be used as an aid in planning further assessment."

The SCREENER system is made up of four main components:

(i) a knowledge base with a set of rules for assessing in general terms the types of

environmental components affected by particular activities, the conditions under which these impacts will occur, and potential mitigative actions available.

(2) detailed descriptions of the specific environmental components at the sites where the screening system will be used.

(3) the screening system itself which takes the knowledge base and environmental setting information and, along with specific project information from the user, determines the potential for environmental impacts. Potential impacts are


characterized by qualitative categories such as significant, insignificant, mitigable, unknown or non-existent.

(4) a reporting or explanation facility which describes the rationale for the over- all assessment and the details of interactions between specific activities and environmental components.

The first version of the system was developed on an IBM AT in PROLOG and C to screen environmental impacts at airports and was recently delivered in an operational mode to the sponsoring agency, Transport Canada.

b) Identification of ecological objects

In the course of protecting en- dangered species and the environment, nature conservation bodies have to inventory animal and plant species on a regular basis. This process includes the tedious and time consuming job of species identification, which requires some amount of biological expertise typically documented in biological classification books. It is quite straight-forward to ex- tract this book knowledge and build up a rule-based diagnosis expert system for ecological species. A corresponding demonstration system was developed at the University of Hamburg, for the domain of German dragron-flies /24/. For the implementation, the AI language OPS5 was used on a VAX main-frame computer. An image data base was linked to the ex ~ pert system in order to present digitalized images of species to the user to facilitate interpre ~ tation.

c) Assistance in the Interpreta' tion of Laws and Regulations

tion of data concerns legal information. The aims of these expert system approaches are to guide in the interpretation of environmental laws and regulations and to eventually provide a transparent and consistent assistance in the preparation or release of environmental permits. Such a reliable, consistent and complete analysis of the relevant legal information should lead to a more balanced permit practice. The rule based nature of such regulations makes them readily compatible with the rule-based representation of the knowledge used in many XPSs.

Expert System for Assistance in Handlinq Environmental Requla- tions - Siemens Inc., MOnchen, aaI~.~-_ __ qPAiu __ ~ j ] i

This XPS approach developed at the Siemens Company is assisting users with limited legal experience to handle environmental regulations and identify the relevant environmental documents for a decision situation such as the planning of a technical plant extension. The system contains information on which regulations are applicable and provides cross references between the regulations, and plant information. The expert system approach was introduced for regulations under the German Federal Immission Pro- tection Law and is operational for the purposes of permission procedure classification, re- quirement definition for emission declarations and for emission limit value identification for the plant under consideration. The implementation was carried out in LISP on an AI workstation.

d) Hazardous Waste Management N N m

Landfilling is currently the main method for solid waste disposal by municipalities and industry. Assessments of the potential for

An Analysis of Environmental Ezperl System Applications: B. Paye

environment from landfilled wastes are typically based on bench-scale testing protocols and computer models. However, these estimation procedures are limited to relatively simple disposal conditions and well-defined waste types. In addition, the assessment can be very costly to con- duct.

A number of expert system projects were initiated to guide in the handling of environmentally harmful substances and contaminated disposal sites (see /62/, /15/, /59/, /18/). The following expert system approach dealing with "contaminated site assess~ ment" is in a rather advanced development stage

XUMA-Expert System Environmental on the Hazard of Waste Substances - Nuclear Research Center, Karls- ruhe, F.R.G. (/59/) .

The main task of this system is to assist in the evaluation of hazardous substances and contaminated sites in order to decide on waste deposal and remediation a! ~ ternatives. The evaluation process is carried out in four steps:

(i) Construction of a specific site analysis plan. The plan is based on the knowledge on the former utilization of the contaminated site, or the origin of the waste. A knowledge base contains the measured contaminant levels in various branches, parts of the sites, pro- duction processes as well as the chemicals present in various processes.

(2) Input of physical and chemical analysis data as a basis for risk evaluation.

(3) Evaluation of substance hazard in a comparable loca- tion derived from calcula-

vironmental conditions. The system specifies which other analyses are to be carried out, and how the danger posed by the substance has to be rated.

(4) Consideration of given site conditions in evaluation.

An explanation component supp ~ orting different levels of user comments on the rationale of the resulting evaluation on the basis of facts and rules employed in the inference process.

A prototype was developed in cooperation with the environmental office in the federal state of Baden-W0rttemberg on a LISP- machine (Texas Instruments Explo- rer) using the AI development environment Automated Reasoning Tool (ART). The XUMA system is fully operational /WGE88/, which makes it probably the most advanced environmental knowledge- based approach in theFederal Republic of Germany to date.

e) Wastewater Treatment

Wastewater treatment plants regu- larly experience process upsets which degrade their performance level and may violate regulatory treatment standards. To reduce the detrimental environmental impacts on the receiving water body, early effective corrective action is necessary. This is gen- erally based on a diagnosis of the problem through careful observation of the state of the plant by an experienced operator. Much of this experience can be represented as a collection of heuristic rules. Expert systems can be considered as a useful tool to assist plant operators in coping with difficult or unusual situations at a treatment facility.

An Analysis of Environmental Ezpert System Applications: B. Pa#e

Expert System for the Diagnosis of Wastewater Treatment Plants - McMaster University, Hamilton, Onhario, Canada (/39/).

A diagnostic fault detection expert system is under development at McMaster University in Hamilton to assist operators in diagnosing plant upsets in conventional activated sludge. The rule-based XPS provides operators with the most probable cause and appropriate corrective actions associated with a given state of an activated sludge waste-water treatment facility. The knowledge base covers the operation of the primary sedimentation basin, the aeration basin and the secondary clarifier propagating uncertainty by special factors. The knowledge acquisition process used both experienced operators and the literature as sources. The XPS provides the user with the flexi- bility to ask questions about routine visual observations, simple analytical measurements and microscope evaluation of the sludge from the aeration basin. Conclusions reached by the expert system include a qualitative description of the plant state, a list of possible causes (grouped as external, equipment malfunc- tion and improper control) as well as potential corrective actions and a list of other tests and inspections for yielding additional evidence.

The demonstration system was developed using the Personal Con- sultant Plus XPS shell by Texas Instruments. PC Plus operates on a LISP environment.

f) Warning and Consulting Systems for Disturbance and Accident Situations

Accidents with dangerous materials are handled by expert teams which must act quickly and accurate!v to limit danger due to

toxic fumes, chemical interactions, explosions or fires. Expert system technology can be useful in assisting these experts in crisis and accident situations, when a large number of technical data has to be correctly interpreted and evaluated within a limited time and under highly stressful working conditions. Because of the high risks involved in these operations, this type of expert systems application asks for a high degree of system reliabili~ ty. Less critical would be the use for training purposes in emergency reponse.

HERMES - Heuristic Emergency Response Management Expert System -Alberta Research Council, Calga- ry, _Alberta,Canada (/7/,/I/) .

This knowledge based tool was developed to assist emergency response personnel in the management of land-based accident situations involving dangerous goods.

The HERMES system is based on knowledge regarding significant details and countermeasures in specific emergency situations. It utilizes information entered by the user regarding the nature of the accident (materials involved, rate and volume of leakage) and the environmental variables (proximity to population centers, weather conditions). This data is used to estimate hazard levels such as fire and explosion risks, to make recommendations about procedures to follow for contain- ment and possible evacuation, and to determine additional information that should be gathered. One of the main assets of HERMES is its graphical user interface which features different window sections (scene, incident, advised emergency action given, etc.) for user interaction (see Fig. 2).


HERMES Advice System

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Predictive environmental XPSs often combine traditional mathematical models and programs with knowledge about the environmental processes that originated them to form a basis for predictions.

a) Risk Assessment

Risk assessment (e.g. for conta" minated sites or transport of hazardous goods) is an important task and a complex issue in environmental protection. Knowledge based concepts offer a number of advantages for risk assessment. They serve, for example, reposit ~ ories for consensus knowledge, clarify logic and assumptions, handling provide for handling of qualitative and empirical know ~ ledge, and represent methods for dealing with uncertainties.

AERIS - Aid for Evaluating the Redevelopment of Industrial Sites - SENES Consultants Ltd., Rich- mond Hill, Ontario, Canada

AERIS is a risk assessment model that runs within an expert system programming environment. It uses environmental and toxicological information to estimate exposures (and therefore risks) that site users can receive as a result of contaminants present in soil. For sites under consideration for redevelopment the derived exposure estimates can be used to determine soil clean-up guidelines. In the AERIS system information about the contaminant, the site user, and the local environment are integrated to provide a consistent approach to establi- shing soil guidelines. The user is guided by an "intelligent" interviewing facility in providing information about the rede-

The HERMES prototype was developed in a cooperative project by the Alberta Research Council, Alberta Public Safety Services ~n~ ~ m ~ - ~ , t D ~ ~ ~ ~ ~

using a Symbolics-LISP machine and the AI development environment ART. The prototype also includes a tutorial mode that allows a trainee to acquire emergency response skills.

2.3.2 Planning Systems

This class of expert systems in~ cludes those that provide assist ~ ance in planning (i.e., design, configuration) in complex envir ~ onmental situations. To date there has not been much environmental work done in this area so far (i.e. lab analysis schedul- ing, see /29/) which is due to the higher complexity of environmental planning problems as com- pared to diagnosis/interpretation systems.

2.3.3 Prediction Systems

This expert system class concerns the forecast of future consequences of actions or events. velopment scenario to be evaluated. The knowledge-based module uses a set of rules to decide when sufficient information has been delivered and to provide - aids in estimating parameters, or checks for errors in the answers. The information is then passed to the conventional (mathematical) component modules that calculate concentrations of a compound in air, soil, plants and ground water. The system estimates human exposure through ingestion and inhalation as well as calculating total dosage. This is then compa- red to acceptable toxicological levels.

The AERIS system was developed on an IBM-PC using the expert system shell Level .5 for the Industrial Programs Branch of Environment Canada and has been used already to assess clean-up needs at a few Canadian sites.


b) Severe Weather Forecast

Severe weather events are a particular burden on the Canadian environment. Accurate severe wea ~ ther forecasts are only possible when abundant meteorological in ~ formation gathered from weather forecast offices can be assessed and synthesized. The complexity of weather forecasting, based on a combination of numerical comp ~ utations and empirical rules, makes it a useful field for the introduction of knowledge based techniques. One system of this type is METEOR, a rule- and frame-based XPS for short-term severe storm forecasting developed at Alberta Research Coun- cil, Calgary, Alberta, Canada (see /9/, /i0/). It uses stati- stical models, which are an integral part of an expert's problem solving method in weather forecasting, in conjunctionwith qualitative data and knowledge.

c) Expert Systems as Interfaces to Mathematical Environmental Models

Mathematical modeling of environmental problems represents a long scientific tradition as well as a positive record of application in environmental protection both as an analytical and a simulation tool. Knowledge based systems can provide an intelligent interface to conventional quantitative mo ~ dels of environmental processes. An expert system can guide a non-specialist user through the process of determining inputs to a model, running a model, and in ~ terpreting the results.

Expert ROKEY Computer System - An expert system for contaminant hydrogeology - Simco Groundwater Research Ltd., Edmonton, Alberta, Canada(/i/) .

The Expert ROKEY system was deve-

in estimating subsurface distribution of chemicals discharged from sources such as sanitary and industrial landfills, leaking underground storage tanks and pipelines, and spill sites. The system can be used in studies of existing sources of groundwater contamination, the selection of proposed sites for waste disposal facilities, and in actual emergency response situations.

The Expert ROKEY system contains a mathematical contaminant transport model calculating the aqueous phase distribution of a chemical in a groundwater flow system, two knowledge-based components, and a plotting package. One knowledge-based component assists users to prepare a set of input data for "the transport model; the other one helps users plan a monitoring strategy for a first-stage field investigation.

The system was written in procedural languages (FORTRAN 77, C) on a standard microcomputer.

2.3.4 Integrated Systems

This category addresses integrated or "hybrid" systems which combine (quantitative, algorithmic) model based decision support systems with embedded (qualitative, heuristic) expert system technology as discussed at the end of section 2.1. Forest fire management is one important application.

a) Forest Fire Management

Forest fires are a severe threat for the environment particularly in Canada, and fire control personnel need a lot of experience in fire crisis management. Expert system technology along with more traditional concepts such as mathematical/simulation modeling, databases, geographical information system, or computer graphics ' - ~ - - ~ - - ~ .... ~ ~ ~ 4 ~ ~ l ~ c c

An Analysis of Environmental Ezpert System Applications: B. Paye

experienced dispatchers in assigning fire control resources to forest fires by providing them with the knowledge of experienced dispatchers. Due to the magnitude of the problem these systems have a high level of development in Canada (see /19/).

b) Reservoir Management

Another application field for AI -based decision support is reservoir management and operations, another environmental en~ gineering topic. Reservoirs are used for spatial and temporal re ~ distribution of water quantity and quality. An example system is REZES, an interactive, menu- driven consultation program de ~ signed as a rule-based advisory tool for model based reservoir analysis. The system, programmed in PROLOG, provides advice in problem formulation, data prepar~ ation, model (FORTRAN) program running, and output presentation 1111.

3. SUMMARY AND EVALUATION

The discussion of environmental expert system approaches from Ca ~ nada and West-Germany in the preceding section and the summary in Table 1 show a clear emphasis on diagnosis/interpretation sy ~ stems (ii out of 21) in problem areas such as environmental assessment, species identification, interpretation of environmental regulations, management of hazardous substances (the most widely addressed issue), failure diagnosis of waste water treatment plants, and guidance in accident situations.

Relatively little work has been done in the (environmental) planning (configuration, design) XPS domain so far. The only appropriate application covered in this context is the construction

of environmental lab analysis schedules. Other (U.S.) projects deal with knowledge-based work assignment and work plan memo generation in waste site clean-up operations (/44/, /27/). Another useful environmental pro~ blem area for potential planning XPS use would be the configuration of dangerous goods storage or transport (e.g. ship loadings). These applications are very much alike traditional planning systems in other technical fields (e.g., computer system configuration). Environ- mental planning problems, involving ecological knowledge, however, do not seem to be suf~ ficiently structured and there~ fore not yet ready for XPS applic~ ation.

The prediction XPSs cited here mostly link knowledge-based components to traditional mathematical models. A highly relevant environmental problem area is the "intelligent" interfacing of mathematical models to facilitate their use. Risk assessment and storm forecasting, also employing quantitative models, are other predictive problem solving domains covered in this study. This application spectrum coin- cides closely with the American environmental XPS work cited in /27/.

The current lack in specialized environmental training/ instructional systems, most useful for knowledge transfer in environmental protection, is expected to be only temporary.

Finally, several integrated or "hybrid" environmental systems were developed which enhance large scale, model-based decision support systems with some restricted knowledge-based techniques (mostly in rule-based knowledge representation) to improve user guidance. Problem areas covered by this Cana- dian work are forest fire and


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eservoir management. The fire ontrol systems already show a igh degree of proficiency and re to be rated on a similar evel as the reknown decision upport system for hazardous ubstances and industrial risk anagement by the International nstitute of Applied Systems nalysis /17/. This good perfor- ance, however, is mainly due to he competent employment of well stablished traditional concepts databases, modelbases, multicri- erial optim-ization, gra- hics, sophisticated user inter- aces), not so much to expert ystem technology which only as a marginal function.

Table 1 and section 2.3 show hat the XPS implementations are sing mostly XPS shells or AI ~nguages such as LISP and PRO- 3G, interfaced to procedural ~nguage programs (e. g. FORTRAN/ "models). Much effort has gone ~to the design of the (partly raphical) user interfaces. Many ~stems run on microcomputers almost exclusively on IBMs and 3mpatibles) or on AI worksta- ions. In the future, the trend s likely to be towards work- tations for development of en- ironmental XPSs, incorporating imulation modeling with a higher emand for computing power /27/. Despite this identification of

1 Canadian and German XPSs, hich were not included in ushon's 1987 U.S. survey, her oncluding statement that only ery few environmental XPSs were n practical use by that time, .olds just as true today. Inter- ~stingly enough, the operational IPSs tend to be those which :nhance traditional methodology such as simulation modelling or 2isk analysis) with some know- [edge-based functions (mostly of ~he rule-based type). Several )ther systems are still only operational prototypes and many

tion packages or still under early development.

Many systems originating from universities and research insti- tutions have not been applied to real-world environmental problem solving and will probably never be. However, some substantial work has also been done by com ~ mercial companies on pragmatic grounds, which is usually more rapidly applied and readily adapted to practical require- ments.

4. CONCLUSIONS

Expert system technology has some attractive features to offer, making it suitable for a role in environmental problem solving. Indeed, expert systems are being developed and sometimes in use already for a variety of problem areas in this field as discussed in the last two sections. Taking into account that

- environmental XPS applications are still in an initial phase and well behind other application domains such as busi- ness, manufacturing, medicine or computer systems engineering (see /43/),

- environmental problems show a high degree of complexity, interdependency and multi- disciplinarity,

we should have in mind more modest goals for "real-world" XPS applications in environmental protection. With current XPS techniques and tools we should concentrate on the following areas:

(i) More "intelligent" (i.e. AI- based user interfaces employing coloured graphics, object


systems, flexible help functions on different user levels, explanation facilities and eventually performing some natural language processing.

(2) "Intelligent" user access support and orientation on environmental databases (e.g. literature, see /KF~85/, or chemical substances) as well as more efficient database search techniques.

(3) Knowledge based front ends to already existing quantitative models guiding the selection of an appropriate, problem specific model and its proper use.

(4) Training and instructional systems allowing for an efficient transfer of rare environmental expert knowledge, e.g., in emergency response.

(5) Straightforward diagnostic/ interpretation XPSs for well bounded domains, ~hich are well understood in AI; and where powerful tools are already available, as problem solving aids (e.g., in early environmental impact assessment stages).

(6) Enhancement of traditional, model based decision support systems which are likely to maintain a high r~:levance in the environmental field, by

knowledge-based techniques.

Emergency response is definite- ly an application ~rea where expert systems would be most helpful in guiding less experienced emergency staff especially in time critical emergency situations based on scenacios worked out by experts ahead of time. This would also held durinq the

initial phase until real experts became available. Also, environmental emergency cases often require multidisciplinary expertise (e.g., environmental chemistry, analytical chemistry, toxicology, environmental biology) which, if available in knowledge bases, could supplement specialized expertise typically performed by field experts. However, it is doubtful at at this time whether current maturity of XPS technology allows for real-world employment in high risk emergency response decision making. At this time, there are certain inherent XPS limitations that might lead to failure in some situations. There still persists an acknowledged "knowledge acquisition bottleneck" as one of the main problems in XPS development, which is likely to be even more acute in the complex; fragmented multidisciplinary, and rapidly evolving area of environmental protection. Thus, an XPS design may not cover all the possibilities or contingencies of a problem domain. A human expert faced with such a problem falls back on common sense and general background knowledge. XPSs typically contain expert knowledge on specific, well-bounded domains and cannot handle common sense knowledge yet. At the boundaries of their expertise, XPSs become sud- denly incapable rather than gradually less proficient at problem solving ~ike human experts do, who rely on their basic knowledge of neighbouring domains or on their general background knowledge. The expert knows what makes sense in such a situation, the XPS does not /19/. Other weaknesses of current expert systems are the validity problem of com-plex knowledge bases, the maintenance problem in rapidly evolving domains wit~ little stability in


knowledge and the poor explanation facilities which are not sufficient for non-experts.

There are a number of specific research needs for knowledge- based systems to overcome some of the current deficiencies in their application on environmental problems. These are:

(i) Representation and integration of different knowledge types and problem solving strategies: many environmental problem areas involve a variety of knowledge repre- sentations and problem solving approaches which demand too much of monolithic concepts /43/. On the other hand lack of transparency becomes a major obstacle.

(2) Architectures for interfacing and coordinating several different knowledge bases (e.g. distri- buted XPSs) : multidisciplinary environmental domains necessitate the integration of various, independent expert knowledge sources /23/.

(3) Spatial and temporal reasoning: environmental pheno- mena are often dynamic and time-dependent and related to particular regions. This task requires a framework to support inferences on temporal and spatial features of environmental activities /i0/, /8/.

(4) Qualitative simulation: complex ecological processes cannot always be adequately described in numerical terms. Their structure can be simplified by describing parameter relations qualita- tively /43/.

(5) Interfacing XPS with geographical information systems

(GIS) : because environmental information is often spatial, GISs are commonly used for data management and mapping geographic data. In a large scale knowledge-based environmental information system, the access to regional data via a GIS interface as well as the handling of spatial data would be required /45/, /46/.

(6) Further enhancement of XPS tool/shell functionality (e.g., morepowerful knowledge repre- sentations, more problem solving strategies), and interfaces to conventional software systems such as data bases, GIS's, simulation systems, etc.

(7) Improvement of knowledge acquisition techniques and tools: XPS development in the multidisciplinary environmental field requires a more complicated acquisition from multiple experts /34/. This complexity is mainly due to the difficulties in syn- thesizing their differing points of view which might require a more varied knowledge representation as well.

With substantial progress in these research areas (as well as in the development of appropriate software) we can hope to fully exploit the potential of XPS technology in the environmental field.

Acknowledgements:

This comparative study was mainly carried out at the Alberta Research Council in May~June 1989 and was funded by a Faculty Research Award granted by the Canadian Department of External Affairs, Ottawa.

AnAnalysis of Environmental Ezperl System Applications: B. Page

This support is gratefully acknowledged.

I also thank Julia Driver, Arti- ficial Intelligence Researcher at the Alberta Research Council, for literature support and helpful comments on the draft of this manuscript. I am grateful as well to Greg Sidebottom from the Alberta Research Council, and to Bob Everitt and Nick Sonntag from Environmental and Social Systems Analysts Ltd., Vancouver, for demonstrating their expert systems. Last not least I am grateful to one unknown referee of this journal for his careful review and his most valuable comments.

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An analysis of environmental expert system applications