Isaac Wong

Design and implementation of an environmental decision support system

Abstract An environmental decision support system is a specific version of an environmental information system that is designed to help decision makers, managers, and advisors locate relevant information and carry out optimal solutions to problems using special tools and knowledge. The RAISON (Regional Analysis by Intelligent Systems ON microcomputers) for Windows decision support system has been developed at the National Water Research Institute, Environment Canada, over the last 10 years. It integrates data, text, maps, satellite images, pictures, video and other knowledge input. A library of software functions and tools are available for selective extraction of spatial and temporal data that can be analysed using spatial algorithms, models, statistics, expert systems, neural networks, and other information technologies. The system is of a modular design which allows for flexibility in modification of the system to meet the demands of a wide range of applications. System design and practical experiences learned in the development of a decision support system for toxic chemicals in the Great Lakes of North America are discussed.

The wildspace™ decision support system

Abstract A system architecture was developed for the wildspace decision support system (DSS) to provide a better understanding of complex wildlife and habitat problems. The system makes use of two key concepts, SPECIES and SPACES, to define the study domain. wildspace DSS’s flexible user interface allows users to select SPECIES through a number of different approaches, including direct selection and selection using information such as avian life history and project metadata. On the SPACES side, the system uses the raison ™ object system (ROS) for mapping functions and spatial analysis. The key element in wildspace DSS is its knowledge-based database manager that provides intelligent support to various components of the system. It keeps track of all the legitimate databases, provides intelligence within the SPECIES and SPACES selection process and, more importantly, interfaces with the knowledge templates which are sets of operations implementing pre-defined analysis routines used for integrated analysis. This integrated decision support approach allows users to combine a diverse set of tools within a common framework. wildspace DSS is used to study complex wildlife problems involving multiple projects and data that are temporally and spatially heterogeneous. A case study about a relevant wildlife conservation question is presented using a series of queries and analyses performed within wildspace DSS. The system also serves as the repository for all past, current and future wildlife data collected by the Canadian Wildlife Service—Ontario Region.Abstract A system architecture was developed for the wildspace decision support system (DSS) to provide a better understanding of complex wildlife and habitat problems. The system makes use of two key concepts, SPECIES and SPACES, to define the study domain. wildspace DSS’s flexible user interface allows users to select SPECIES through a number of different approaches, including direct selection and selection using information such as avian life history and project metadata. On the SPACES side, the system uses the raison ™ object system (ROS) for mapping functions and spatial analysis. The key element in wildspace DSS is its knowledge-based database manager that provides intelligent support to various components of the system. It keeps track of all the legitimate databases, provides intelligence within the SPECIES and SPACES selection process and, more importantly, interfaces with the knowledge templates which are sets of operations implementing pre-defined analysis routines used for integrated analysis. This integrated decision support approach allows users to combine a diverse set of tools within a common framework. wildspace DSS is used to study complex wildlife problems involving multiple projects and data that are temporally and spatially heterogeneous. A case study about a relevant wildlife conservation question is presented using a series of queries and analyses performed within wildspace DSS. The system also serves as the repository for all past, current and future wildlife data collected by the Canadian Wildlife Service—Ontario Region.

Species at risk: Data and knowledge management within the WILDSPACE™ Decision Support System

Abstract As part of its commitment to conserve biodiversity, the Canadian government passed legislation in 2003 for the protection and recovery of wildlife and plant populations at risk of extinction in Canada. There is currently no single system to store, retrieve and interpret information on species and their critical habitats in support of this legislation (i.e. the Species at Risk Act). In order to meet the information requirements for the Species at Risk Act (SARA), it is highly desirable to develop network designs, infrastructures and applications that link distributed data sources into an integrated system that manages data and provides decision support. The system architecture described here will be built on the versatile WILDSPACE™ Decision Support System (hereafter referred to as ‘WILDSPACE DSS’) and will be web-based consisting of distributed servers (database servers, web servers and map servers) providing different kinds of information including species and habitat data, geo-spatial data, metadata, web services and decision support analyses. Its design takes into consideration the needs of different user groups (Intranet, Extranet and Internet) and data security. The complexity of Species at Risk data requires considerable “best practice” database design efforts that strike an optimum balance among storage, maintenance, and application performance. The WILDSPACE DSS provides an effective platform for the delivery of information and services to Species at Risk practitioners for better decision-making through its data mining and modeling functionality.

A decision support system for environmental effects monitoring

The Environmental Effects Monitoring (EEM) Statistical Assessment Tool (SAT) Decision Support System (DSS) has been developed to provide a user-friendly data analysis, display and decision support tool for Canadas federal environmental effects monitoring program for the pulp and paper and mining industries. The target users include industries, consultants, regional EEM coordinators, National EEM Office and scientists involved in EEM-related research. The tool allows the assessment of the effects of effluent from industrial or other sources on fish and benthic populations. Effect endpoints, which are used as indicators of potentially important effluent effects, are measured at effluent-exposed sites and are compared statistically to measures at reference sites, in order to determine if changes have occurred and the magnitude of the changes. The main driver of the EEM-SAT DSS is its rule-based expert system. The results are used in assessing the adequacy of existing regulations for protecting aquatic environments.

A knowledge-based approach to regional acidification modelling

Data from over 2000 stations and knowledge from experts on atmospheric transport, soil geochemistry, lake chemistry, wetland processes and acidification modelling were assembled in an expert system. The data were grouped by aggregates of tertiary watersheds based on water chemistry knowledge. A set of expert rules was used to determine which of six existing models was most appropriate for a given set of data. Comparison of computed and observed alkalinity indicated median relative errors from 11.3–17.9%, with regression slopes ranging from 0.91–1.18 and regression coefficients between 0.82 and 0.99. The expert model performance was further confirmed with paleolimnological data and other independent sets of data. The sensitivity of the predicted alkalinity was illustrated by changing some of the rules. Given that the rules were acceptable by experts and produced reasonable agreement with observations, the knowledge-based system seemed a viable approach to the impact assessment of acidic deposition.

The predicted effect of SO2 emission controls on the water quality of eastern Canadian lakes

Changes in SOinf4sup2-deposition predicted to occur in response to implementation of announced SO2 emission control programs in Canada and the U.S.A. have been used as input to water chemistry models thereby giving an estimate of the changes in lake acid neutralizing capacity (ANC) and pH that can be expected from these programs. Eastern Canada has been divided into 22 subregions for the purpose of this analysis. Relative to the current level (1982–86) of SOinf4sup2-deposition (Scenario 1), the effect of the Canadian SO2 emission control program alone (Scenario 2) is compared to that obtained when controls are implemented throughout North America (Scenarios 3 and 4). SO2 emission reduction will effect a shrinkage of the high wet SOinf4sup2-deposition field in NE North America such that under Scenario 4 conditions, almost no area will remain in Canada that receives >20 kg ha-1 yr-1. The greatest decrease in deposition and resulting change in lake chemistry occurs in southern Ontario and southwestern Quebec. ANC distributions shift to higher concentrations and the percentage of lakes having pH<6 decreases in these areas. The Atlantic Provinces will obtain only a minor benefit from the control programs, i.e. experiencing only a small decrease in deposition and improvement in water quality. High sensitivity of the terrain in many parts of Atlantic Canada means that large numbers of lakes will remain acidic (i.e. ANC<0) and/or have pH<6 (an important biological threshold) even after full implementation of the current plans for SO2 control in Canada and the U.S.

Case Studies of Canadian Environmental Decision Support Systems

This chapter will discuss two different decision support systems that we have developed for Canadian environmental applications. We will first discuss how these systems utilize data and models to solve domain-specific problems and focus on effectiveness rather than efficiency in the decision making processes. In particular we will discuss how they are useful in better understanding the complex interaction between land and water and how they also provide a method to make informed resource management decisions and that they require the integration of scientific data, information, models and knowledge across multimedia (air, land and water), multi-disciplines and diverse landscapes. We will discuss how modelling is an important asset of any environmental decision support system (EDSS), particularly considering the high cost of full scale field work. Modelling presents a cost effective approach to assess the impact on the environment. We will discuss how a typical EDSS needs to be developed to address the issues of linking multi-media models at different geospatial scales, how it provides interfaces that can accept, select, link and recalibrate discipline-specific component models, and how it can seek optimal solutions for a given domain problem. We will also discuss that very often the EDSS is built around the concept of a management user interface to assist policy makers in their decision making. The technical users employ other tools to build model inputs, execute, and calibrate and validate the models while the management or policy makers view the inputs and outputs of the system that the technical users have built. This will allow management to investigate the analytical results based on robust science built by the researchers. Key functionality includes mapping and visualization of the results, scenario gaming and key statistical analyses of the results. The first example we will discuss is the Environmental Effects Modelling Statistical Assessment Tools Decision Support System. We will discuss how it provides a user-friendly data analysis, display and decision support tool for Canada’s federal environmental effects monitoring program for pulp and paper and mining industries. We will describe how the tool allows the assessment of the effects of effluent from industrial or other sources on fish and benthic populations. We will explain that in many of our EDSS systems, it is coupled with artificial intelligence such as expert systems to guide the users in the right direction. We will explain how the results are used in assessing the adequacy of existing regulations for protecting aquatic environments. We will explain how the design of such an EDSS has benefited from significant input from scientists, researchers, other end-users, system

DATA AND KNOWLEDGE VISUALIZATION IN AN ENVIRONMENTAL INFORMATION SYSTEM

An environmental information system is an important tool in conducting ecosystem research that integrates data and knowledge from many disciplinary studies. Effective computer visualization of these data and knowledge leads to better communication and understanding of the individual results and the concept of the ecosystem approach. By using examples from an environmental information system prototype, the advantages and issues surrounding the development and application of visualization methodologies will be discussed. It was found that while data visualization techniques have advanced significantly, the knowledge representation and visualization procedures have not been fully developed. Further work in this area was recommended particularly in view of the demand for better knowledge exchange mechanisms among scientists with different disciplines.

An expert system for water quality modelling

The RAISON-micro (Regional Analysis by Intelligent System ON a micro-computer) expert system is being used to predict the effects of mine effluents on receiving waters in Ontario. The potential of this system to assist regulatory agencies and mining industries to define more acceptable effluent limits was shown in an initial study. This system has been further developed so that the expert system helps the model user choose the most appropriate model for a particular application from a hierarchy of models. The system currently contains seven models which range from steady state to time dependent models, for both conservative and nonconservative substances in rivers and lakes. The menu driven expert system prompts the model user for information such as the nature of the receiving water system, the type of effluent being considered, and the range of background data available for use as input to the models. The system can also be used to determine the nature of the environmental conditions at the site which are not available in the textual information database, such as the components of river flow. Applications of the water quality expert system are presented for representative mine sites in the Timmins area of Ontario.

A tight package wrapping for planar point-sets

Abstract Three algorithms for generating the convex hull of a discrete point-set in the plane are extended to a non-convex wrapping to follow concavities in the points distributions. Complexity of computation is derived along with actual performance on generated data sets of varying cardinalities. Some postulated anomalies are removed, with examples. Two real-life examples of the use of the algorithms are presented: a Lagrangian particle simulation of a pollutant plume on the surface of a large lake, and a visualization of a two-dimensional point-set of water quality data showing the population of anion-cation pairs of observations of lakes and rivers for various tertiary watersheds in Eastern Canada.