Expert Systems and Geographical Information Systems for Impact Assessment - Chapter 5

5 GIS and expert systems for impact assessment 5.1 INTRODUCTION Chapters 3 and 4 reviewed a wide range of GIS applications to environ-mental matters, also showing how limited their capabilities are when used on their own and without pre-programming. This chapter discusses the use of expert systems (ES) technology, in particular in combination with GIS, arguing that “partial” technologies like GIS maximise their contribution within the framework of decision-support tools. The chapter first discusses the use of ES without GIS, and then with GIS, in Impact Assessment and environmental management, following the same distinction used when reviewing GIS applications in the previous chapters. Decision support systems are discussed afterwards.13 In contrast to the previous review of GIS applications, ES and decision-support technologies are more novel and the proportion of references appearing in research journals and books – as opposed to magazines and conference papers without follow-up publications – is much greater, a reflection of the greater research interest these types of GIS applications still have. Another consequence of this is that the proportion of publications discussing methodo-logical issues is far greater than that in more established types of GIS use. 5.2 EXPERT SYSTEMS WITHOUT GIS FOR ENVIRONMENTAL ASSESSMENT It is interesting that, in parallel to ES not making inroads in areas like town planning – as already mentioned – such systems seem to be attracting fresh interest in new areas like IA and environmental management. The process appears to be starting all over again in this new field, with articles highlighting 13 Rodriguez-Bachiller (2000) includes an earlier version of this bibliographical review. © 2004 Agustin Rodriguez-Bachiller with John Glasson GIS and expert systems for IA 117 the potential of ES appearing in the environmental literature, and proto-types starting to be developed and used. 5.2.1 Expert systems without GIS for impact assessment Looking first at IA as such, most of the early articles performed what can be called an eye-opener function, and at the same time some were monitoring what was happening (like Spooner, 1985, in the US Environmental Protec-tion Agency), some were pointing out the potential of ES for IA in general (Chalmers, 1989; Lein, 1989), and some were pointing at particular areas of IA: • For project screening (determining if a project requires an impact assessment study), Geraghty (1992) reviewed briefly some systems in Japan, Italy and Canada and proposed the GAIA system, an ES for guidance to help assess the significance of likely impacts from a project in order to see if an Environmental Statement is needed. Later, Brown et al. (1996) developed it into the HyperGAIA system (which they labelled as decision support system) to diffuse IA expertise, and they used project screening as an example. This group of researchers have made the issue of expertise and its diffu-sion, central to ES, their main focus of interest, even if their discus-sions are not always linked to any computerised system in particular: Geraghty et al. (1996) are interested in the future use of guidance manuals for EIA (which can be seen as “paper” ES), and Geraghty (1999) undertakes a comparative study of guidance docu-ments to support practice. • For the scoping of project impacts (identifying the impacts to be studied and how “key” they are), Fedra etal. (1991) provide an early example for the Lower Mekong Basin in South-East Asia, and Edward-Jones and Gough (1994) developed the ECOZONE system to scope the impacts on agriculture of projects of any kind. • For impact prediction as such, Huang (1989) developed the early system MIN-CYANIDE for the minimisation of cyanide waste in electroplating plants, and Kobayashi etal. (1997) incorporate environmental considerations in an ES to help with the location of industrial land uses. • For the review of Environmental Statements, Schibuola and Byer (1991) proposed the REVIEW system (written in Prolog) to overcome the problem of Environmental Statements being reviewed in an ad hoc way, and he illustrated the system concentrating on only one aspect of ES: the consideration of alternatives for a project. • Echoing similar developments in other areas (like GIS), Hughes and Schirmer (1994) point out the potential of expert systems for public participation in IA as part of an interactive multimedia approach. © 2004 Agustin Rodriguez-Bachiller with John Glasson 118 GIS and expert systems for IA 5.2.2 Expert systems without GIS for environmental management In the more general area of environmental management, a few “eye-opener” articles on the potential of ES have been appearing since the 1980s (Hushon, 1987; Borman, 1989; Lein, 1990), while some early prototypes were already being developed mainly to help with two types of tasks: • Environmental analysis, where geology is quite prominent: Krystinik (1985) proposed a system for the interpretation of depositional envi-ronments, Fang and Schultz (1986) and Schultz etal. (1988) discuss the XEOD system for the geological interpretation of sedimentary environ-ments, and Liang (1988) developed a system for environmental analysis of sedimentation; Miller (1991) applies a system to sedimentary basin analysis, while Besio etal. (1991) apply a non-geological ES to classify and analyse the landscape in an area. • Management as such: Coulson etal. (1989) designed a system for pest management in forests, Greathouse etal. (1989) applied to environ-mental control a system for land management developed earlier (Davis etal., 1988) and, more recently, Clayton and Waters (1999) also developed a land management system, for the Northwest Territories in Canada. These are just a few examples. Fedra etal. (1991) review a number of early projects from the 1980s combining ES and hydrologic modelling, and a comprehensive review of environmental management expert systems in the 1980s can be found in Warwick etal. (1993). 5.3 EXPERT SYSTEMS WITH GIS Turning now to ES in combination with GIS, the notion of linking GIS technology to other advanced tools like expert systems was already emer-ging in the early 1990s, as calls for so-called “intelligent” GIS were frequent and in wide-ranging arenas (Laurini and Milleret-Raffort, 1990; Burrough, 1992; Openshaw, 1993a). Eye-opener articles were starting to suggest the types of structures that such combined systems would have, and also start-ing to show examples of ES–GIS combinations (Smith etal., 1987; Bouille, 1989; Heikkila etal., 1990; Fedra etal., 1991; Lam and Swayne, 1991; Evans etal., 1993; Leung and Leung, 1993a; Vessel, 1993), not forgetting the considerable difficulties involved in linking these two technologies, which were identified at quite an early stage (Navinchandra, 1989). Because of the greater novelty of this technology in the early 1990s (at least in this field), there was a greater emphasis on methodological issues than for GIS alone (see previous chapters), which had undergone similar © 2004 Agustin Rodriguez-Bachiller with John Glasson GIS and expert systems for IA 119 30 Methodology Application 25 20 15 10 5 0 88 89 90 91 92 93 94 95 96 97 98 99 Figure 5.1 The change of emphasis from methodology to application. methodological discussions a decade earlier but are now raising more issues about their diffusion than about their methodology. Figure 5.1 shows the frequency in GIS–ES usage of methodological and applica-tion references during the 1990s expressed as percentages of all envi-ronmental GIS references reviewed each year (see Rodriguez-Bachiller, 2000), and we can see how the methodological emphasis in the early 1990s gradually fades away and is replaced by discussions of practical applications. 5.3.1 GIS and expert systems: methodological issues What dominated the methodological discussion in those years was undoubtedly the question of how to integrate ES and GIS, and many authors contributed to that debate in the early 1990s (Webster, 1990a; Fedra etal., 1991; Smith and Yiang, 1991; Zhu and Healey, 1992; Fischer, 1994), mapping out the possible forms of integration between the two technologies – in a way similar to earlier discussions about linking GIS with models: • ES logic can be used simply to enhance the GIS database with rules. • An ES (the same as a model) can be “loosely coupled” with an external GIS, calling its database through an interface. • Using “tight coupling”, one of the two technologies can be a “shell” for the other and run it: the ES can be running the GIS or the GIS can run the ES. © 2004 Agustin Rodriguez-Bachiller with John Glasson 120 GIS and expert systems for IA • In full integration, ES operations can be built into GIS functionality (or spatial information handling can be built into the ES, although that is much more difficult). Related to the problem of GIS–ES integration, the development of suitable interface tools for the connection, usually in the form of “shells” which could talk to both technologies (Buehler and Wright, 1989; Maidment and Djokic, 1991; Leung and Leung, 1993b) also attracted considerable attention, a prominent example being the interface written by Maidment and Djokic to connect the NEXPERT expert-systems “shell” and the Arc-Info GIS. Apart from the form of the integration between GIS and ES, the issue of knowledge acquisition is ever-present in ES work (as discussed in Chapter 2) and the addition of GIS adds the spatial dimension to the problem of extracting knowledge, be it from experts (Waters, 1989; Webster etal., 1989; Cowen etal., 1990; Linsey, 1994), from past case-based experience (Holt and Benwell, 1996), or directly from a database (Deren and Tao, 1994). Apart from methodological problems arising from ES–GIS integration, ES (and AI) have been used to address a series of cartographic problems in GIS work, mainly in areas having to do with visualisation presentation of maps, and with the interpretation of certain type of data. 5.3.1.1 Methodological issues: visualisation The visualisation problem that has probably attracted most attention in connection with the use of AI techniques with GIS has been that of map generalisation, central to any cartographic system where a decision has to be made each time a map is produced, at a given scale, about how much detail to use at that scale. Such decisions can be about what to include (what sizes of settlements to leave out, for instance), or in terms of how to represent lines (line generalisation) on the map.14 To deal with this problem, two different types of AI approaches have been explored, with unequal interest: • Using neural networks (see Chapter 2) started to attract interest in the late 1980s, to generalise settlements (Powitz and Meyer, 1989) or for general-purpose line generalisation (Pariente, 1994; Werschlein and Weibel, 1994). • But, by far, the most researched approach to “intelligent” map general-isation is rule-based – similar to how ES work – sometimes involving “knowledge acquisition” (Muller and Mouwes, 1990) to determine the 14 The issue of how much detail to use when representing a line at a particular scale leads directly to the perplexing realisation that at different scales, lines appear to change in length as their scale of representation changes, and the concept that links these two variables (scale and size) is that of fractal dimension, which opens the door into the field of fractal analysis, fascinating in itself and with wide-ranging ramifications (an easy introduction to the subject can be found in Lauwerier, 1987). © 2004 Agustin Rodriguez-Bachiller with John Glasson ... - tailieumienphi.vn