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Part II
Building expert systems (with and without GIS) for impact assessment
II.1 INTRODUCTION
The picture developed in the previous chapters suggests that IA is evolving in a way that might benefit from increased automation. At the same time, computer technology is becoming more adaptable and user-friendly for practical problem-solving.
Good practice and expertise in IA seem to be now well established in the UK, as indicated by the establishment of accepted standards of content and procedure in Environmental Statements (DoE, 1995, 1996), and also the appearance of a “second generation” of publications – the new IA regula-tions (DETR, 1999), new editions of classic texts like Glasson etal. (1999) and Morris and Therivel (2001) – all suggesting that IA seems to be reach-ing what we could call its maturity.
Expert systems combine rather elegantly the ability to crystallise accepted expertise and a degree of user-friendliness which make them good vehicles for technology transfer when applied to the solution of specific problems, such as those that appear in IA. On the other hand, expert systems cope best with relatively small problems, and the complexity of these systems can grow with the complexity of problems only up to a point. Beyond a certain degree of complexity, rather than having an expert system to deal with all the issues, experience suggests (Rodriguez-Bachiller, 1991; Hartnett etal., 1994) that a natural “division of labour” between expert systems (or parts of an expert system) exists, and a “modular” approach to ES design is likely to work better. Some expert systems can be designed to deal with specific problems, while other (“control”) systems can deal with the general management of the problem-solving process. Such control systems can be themselves expert systems, or they can be part of a more flexible decision support system (DSS), depending on the degree of flexibility needed and on whether “what-if” evaluations are required or not.
GIS are powerful databases which can be useful in dealing with some spatial aspects of IA, especially in the general areas of environmental
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160 Building expert systems for IA
monitoring and management, which provide the backcloth for the more technical core of IA. Experience also seems to indicate that for GIS to perform more technical tasks going beyond the role of “data providers”, they require a considerable amount of expertise and/or programming. This suggests that GIS also can benefit from being linked to other systems (like expert systems) that “manage” their performance. GIS can be used by such systems as data providers, or their functionality can sometimes be used to help solve specific problems in IA.
If we add to this picture the traditional instrument used in the technical core of IA – simulation modelling – the full picture that emerges shows a top-level system (an expert system or a DSS) controlling lower-level problem-solving modules (expert systems are also good candidates for these low-level tasks). These in turn manipulate lower-level tools (like models or GIS routines) to perform specific tasks, relying on data sources provided by databases of various kinds (GIS being one of them).
II.2 STRUCTURE
In Part II we discuss these issues of expert system design applied to specific areas of IA, from project screening and scoping to the treatment of specific impact areas, to the review and assessment of Environmental Statements. We can follow the classic view of ES design in stages as summarised by Jackson (1990) from Buchanan etal. (1983). Jackson’s summary stages refer specifically to “knowledge acquisition” but, in fact, also correspond to the initial stages needed for general ES design:
• identification: identifying problem characteristics
• conceptualisation: finding concepts to represent knowledge • formalisation: designing the structure to organise knowledge • implementation: formulating rules to embody knowledge
• testing: validating rules that organise knowledge.
Beyond these stages, there is “prototyping” (building the first full system) followed by testing, and then successive cycles of refinement. Our discussions in Part II will extend in most cases to the formalisation stage, and only occasionally will go further into implementation or prototyping. In most cases, we shall go as far as what can be best described as “designing a paper-ES”, describing verbally and graphically the structure an expert system would have and indicating how it could be formalised.
To progress in this direction, a knowledge acquisition stage was organised in a well-established fashion, based on the two-pronged approach of con-sulting written documentation and consulting established experts personally. Some of the manuals and textbooks used have already been referred to, and will be mentioned. With respect to knowledge acquisition from experts,
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Introduction 161
two main sources of expertise were used: (i) academic experts with practical experience, in particular, academics in the Impact Assessment Unit (IAU) in the School of Planning at Oxford Brookes University; (ii) practicing impact-assessment professionals, in particular, specialists employed by an inter-nationally recognised firm of consultants, Environmental Research and Management (ERM), with one of their branches in Oxford and another in London. The choice of experts from these sources was made on grounds of superior expertise and the resulting breakdown of experts and topics was:
• project screening: Joe Weston, IAU • scoping: Joe Weston, IAU
• socio-economic impacts: John Glasson, IAU
• air pollution: Roger Barrowcliffe, ERM (Oxford) • noise: Stuart Dryden (Oxford)
• terrestrial ecology: Nicola Beaumont, ERM (Oxford) • fresh water ecology: Sue Clarke, ERM (Oxford)
• marine ecology: Dave Ackroyd, ERM (Oxford)
• soil/geology: John Simonson, ERM Enviroclean (Oxford) • waste: Gev Edulgee, ERM (Oxford)
• traffic: Chris Ferrary, ERM (London)
• landscape: Nick Giesler, ERM (London)
• environmental statement review: Joe Weston, IAU.
Also, consultation of a more general nature about IA was carried out with two of the managers of ERM: Karen Raymond and Gev Edulgee. Repeated interviews were carried out with these experts by Rodriguez-Bachiller, and the “protocols” of these interviews were later amalgamated with relevant technical documentation into the material that provides the basis for the discussion of different aspects of IA in the next few chapters. This first amalgamation was undertaken by the following graduates from the Masters Course in Environmental Assessment and Management at Oxford Brookes University:
• Mathew Anderson: soil/geology
• Andrew Bloore: landscape, air pollution, marine ecology • Duma Langton: socio-economic impacts
• Owain Prosser: terrestrial ecology, fresh water ecology • Julia Reynolds: traffic
• Joanna C. Thompson: noise.
The list of impact types included in Chapter 1 could be used as a guiding principle for the discussion in this Part, but it is preferable to structure the discussion in the next few chapters grouping these areas of IA into themes and/or approaches, relating to the potential ways in which ES, modelling and GIS technologies relate (or could relate) to these impact assessments.
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162 Building expert systems for IA
The sequence of chapters follows an overall framework of IA stages, starting from screening and scoping, then moving on to impact assessment as such – at this stage the discussion “branches out” into various areas of impact – and finishing with the review of Environmental Statements. We start in Chapter 6 with the two related issues of project screening and scoping, which are highly regulated and relatively “easy” subjects for expert systems treatment. In Chapter 7 – the first of the impact assessment chapters – we go to one extreme by discussing areas of impact characterised by “hard modelling”, using air pollution and noise as examples. In contrast, Chapter 8 examines areas where modelling has a lesser role to play: terrestrial ecology and landscape impacts. Subsequent chapters explore “mixed” areas of IA, where modelling is complemented (sometimes replaced) by more low-level techniques: Chapter 9 looks at socio-economic and traffic impacts, Chapter 10 discusses hydrogeology and water ecology. Finally, returning to the main IA process, Chapter 11 applies the same reasoning process to the question of Environmental Statement review. These discussions will help raise some general issues of ES design and GIS use which, together with Part I, provide the material for the concluding Chapter 12.
REFERENCES
Buchanan, B.G., Barstow, D., Bechtal, R., Bennett, J., Clancey, W., Kulikowski, C., Mitchell, T. and Waterman, D.A. (1983) Constructing an Expert System, in Hayes-Roth, F., Waterman, D.A. and Lenat, D.B. (eds) op. cit. (Ch. 5).
Department of the Environment (1995) Guide on Preparing Environmental Statements for Planning Projects, HMSO, London.
Department of the Environment (1996) Changes in the Quality of Environmental Impact Statements for Planning Projects (Report by the Impact Assessment Unit, School of Planning, Oxford Brookes University) HMSO, London.
DETR (1999) The Town and Country Planning (Environmental Impact Assessment) (England and Wales) Regulations 1999, Department of Environment, Transport and the Regions No. 293.
Glasson, J., Therivel, R. and Chadwick, A. (1999) Introduction to Environmental Impact Assessment, UCL Press, London (2nd edition, 1st edition in 1994).
Hartnett, J., Williams, R. and Crowther, P. (1994) Per Pixel Reasoning Using a GIS Closely Coupled to an Expert System to Produce Surface Classifications Based on Remotely Sensed Data and Expert Knowledge, Proceedings of the EGIS/MARI ’94 Conference, Paris (March 29–April 1), Vol. 1, pp. 677–83.
Jackson, P. (1990) Introduction to Expert Systems, Addison Wesley (2nd edition). Morris, P. and Therivel, R. (2001) (eds) Methods of Environmental Impact Assess-
ment, UCL Press, London (2nd edition, 1st edition in 1995).
Rodriguez-Bachiller, A. (1991) Diagnostic Expert Systems in Planning: Some Patterns of System Design, in Klosterman, R.E. (ed.) Proceedings of the Second International Conference on Computers in Urban Planning and Urban Manage-ment, School of Planning, Oxford Polytechnic (July).
© 2004 Agustin Rodriguez-Bachiller with John Glasson
6 Project screening and scoping
6.1 INTRODUCTION
Project screening, to decide if a project needs to go through the EIA procedures (making an Environmental Statement and assessing it) in support of a planning application, is the “gateway” into EIA. It has two important characteristics: first many projects being screened are likely to be found not to require EIA. Therefore, the number of projects screened is likely to be much higher than the number of projects eventually subjected to EIA, and screening is likely to become a routine procedure to which more and more projects are sub-jected. Second, the pressures of project-screening cut across the public–private divide and affect agents on both sides of the development control system. It is engrained in the system that (public) controlling-agencies have the need for adequate project screening, but also private developers can benefit from similar capabilities to “try out” different project configurations and find out if they require extra EIA work, before entering the complicated and expensive development control process.
These two characteristics already suggest the potential benefits of some form of automation of the screening process – for example using ES techno-logy – to alleviate the pressure on both public and private organisations. In addition, project screening also shares some of the typical pre-conditions of “sensible” ES application discussed in Chapter 2:
• the screening process is mostly a regulated one (DETR, 1999a,b);
• expertise consists mostly of the knowledge of the published regulations and guidelines, with relatively minor contributions from experience, in borderline cases or in “grey areas”;
• this problem is virtually “routine” for experts, while it is too compli-cated for non-experts;
• it is a relatively simple problem, taking an expert a few hours at most to determine the grounds on which a project may – or may not – require IA.
For all these reasons, project screening is a good “testing ground” for ES technology, and it is no coincidence that (together with impact “scoping”)
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