Intelligent Decision Support System for Environmental Management System and Applications in the South China Region

Transcription

1 Intelligent Decision Support System for Environmental Management System and Applications in the South China Region Yee Leung 1 & Yuk Lee 2 1 Department of Geography Centre for Environmental Policy and Resource Management, and Joint Laboratory for Geoinformation Science The Chinese University of Hong Kong, Shatin, Hong Kong Phone: Fax: yeeleung@cuhk.edu.hk 2 College of Architecture and Planning University of Colorado, Denver, Colorado, U.S.A. Phone: Fax: ylee@carbon.cudenver.edu Presented at SIRC 2000 The 12 th Annual Colloquium of the Spatial Information Research Centre University of Otago, Dunedin, New Zealand December th 2000 ABSTRACT Environmental Decision Support Systems (EDSSs) have been used recently for tidal flow prediction, water resource management, environmental impact assessment, and regional water quality planning. This paper describes the development and application of an intelligent EDSS to analyzing tidal flow and water quality in the Pearl River Delta. The emphasis of the study is on the efficient management of the hydrodynamic and water quality simulation models, manipulation of data, display and policy analysis. The models formulated are essential to water-borne decision making process, particularly when analyzing a complicated river network such as that in the Pearl River Delta. The EDSS has been used to perform optimal planning of waste water treatment facility in the Pearl River Delta. For surface water pollution control planning, an EDSS with optimization capabilities can be used to explore various planning options before selecting the most cost-effective design for achieving water quality planning objectives. These options include spreading investment over a large number of small treatment plants or to concentrate on upgrading the water quality at a small number, but strategically located, treatment plants. Keywords and phrases: environmental decision support system, GIS, pollution control, tidal river network 1.0 INTRODUCTION In general, decision making problems involve multiple objectives and constraints. Their specifications may be precise or imprecise. A single decision may involve multiple decision makers with value judgements and conflicting interests. It is usually impossible to solve a complex problem by using only procedural or declarative knowledge. Its solution usually requires an intelligent and integrated use of information, domain specific knowledge and effective means of communication. Taking the selection of landfill sites as an example, domain experts may have a set of rules for judging site suitability. They can be expressed as knowledge in declarative form. The judgement may be based on environmental regulations and pressure-group interests may be captured as rules. Inference may be based on socioeconomic, geological, water quality, and hydrological data obtained from a database or derived from

2 procedural knowledge such as the groundwater quantity and quality models, surface water quality models, and other landfill related models. Thus, a sensible decision on landfill sites can only be made on the basis of the interactive utilization of data and knowledge expressed in various forms. Similarly, decision making problems such as transportation planning, new town development, provision of public utilities, resource exploration, and environmental management all involve voluminous information and human expertise. The solution of any of these problems require an intelligent use of data, declarative and procedural knowledge, social processes and interactions among experts and non-experts. Procedural knowledge is effective in system specification, calibration, analysis, forecasting and scenario generation for well specified and structured problems. It is, however, inflexible and ineffective to capture human intuition, valuation, and judgement. Moreover, models and procedures are usually theory rich but data poor, and they cannot communicate efficiently with complex databases and non-technical users. On the other hand, declarative knowledge is effective in representing loosely structured human experience and expertise. It is suitable for inference with concepts, ideas, and values. If used intelligently, it can also be employed to develop intelligent communication with databases and users. Similar to procedural knowledge, the power of declarative knowledge has not been fully utilized in spatial decision making, especially with spatial information systems. This type of knowledge is, however, ineffective to solve highly structured problems. Consequently, procedural and declarative knowledge has to be used integratively with databases (e.g. GIS and remotely sensed images) throughout a decision making process. It is apparent that decision makers need a certain level of intelligence to decide on the use of appropriate type of knowledge and the right kind of information to solve problems. The demand on domain specific expertise, technical know how, and accessibility to data is tremendous. It is almost impossible to make good decisions without powerful systems to provide in an integrative way support in various phases of the decision making process. Figure 1 (Leung, 1997) summarizes what is involved in environmental decision making. Broadly speaking, the information side deals with the collection, representation, storage, retrieval, processing, and display of environmental data. In general it involves the handling, processing, and organization of data for the use of calculating and measuring, as well as for reasoning with, and updating of knowledge. The knowledge side comprises the acquisition, representation and storage of environmental knowledge, as well as the use of knowledge in inference and analysis. In general it deals with the handling of the body of truth, organized information, and principles acquired through experience or association. It serves as the basis for inference, and conversion of information into organized and understandable forms. The interplay of knowledge and information greatly expands the role of conventional information technology in environmental decision making. Apparently, this synergy surpasses the capabilities of present day spatial information systems which concentrate more on the mechanics of data manipulation than analysis and inference. It is very straightforward, no intelligent decision can be made without knowledge. It is absolutely essential that we can fully utilize the complementarity roles of knowledge and information in any decision making. acquisition representation spatial decision making collection representation storage knowledge information storage retrieval inference processing analysis display Figure 1: Interplay of knowledge and information in spatial decision making (adopted from Leung, 1997) The purpose of this paper is to introduce in brief the basic structure of an intelligent environmental decision

3 support system for water quality management. Applications in water quality management in the South China region are used to illustrate the effectiveness of the system. 2.0 ARCHITECTURE OF AN INTELLIGENT ENVIRONMENTAL DECISION SUPPORT SYSTEM (EDSS) There are many ways to build an EDSS. Typical examples are systems such as RAISON by Environment Canada, BASINS by U.S. Environmental Protection Agency, and system by Jakeman and associates of Australian National University. Building it from scratch is a time-consuming undertaking, requiring major commitment of resources and manpower spanning over a long period of time. The main advantage of this approach is that the system can be built for domain specific decision making problems. However, such an EDSS can only be used for a specific problem and its utility must therefore be questioned. A more economical and flexible way is to use the best know-how in software and knowledge engineering to develop an EDSS development environment (shell or generator) so that domain experts can use it to build effectively and efficiently a variety of domain specific EDSSs, and non-technical users can explore model outputs or experiment with various scenarios with user-friendly I/O, query and display facilities. That is, we should have a general development tool which decision makers can use to customize, modify, adapt, and evolve EDSS for solving specific environmental problems. The general framework of such an EDSS development tool is outlined in Fig. 2 (Leung, 1993). Based on this framework, an EDSS with the architecture depicted in Fig. 3 has been constructed (Leung et al., 2000). data DBMS MBMS models Expert System Shell problems users experts knowledge linkages plausible linkages Figure 2: General architecture of an intelligent spatial decision support system (adopted from Leung, 1993) The basic architecture of the EDSS is comprised of eleven components, as shown in Fig. 3. The figure describes the ways in which these components are interconnected in order to perform various tasks that include, from top to bottom, data input, analysis, query, problem classification, deduction, model utilization, analysis results and output. The system architecture focuses on the interconnection among these components. Individually, in the system architecture, component A is the user-machine interface through which the user interacts with the EDSS. It includes the formulation of decision issues, environmental data protection, data I/O, textual or graphic I/O of analysis results, problem formulation and I/O management. Component B is the master controller and functions as a command analyzer and message sender. All commands move from component B to various modules in component C which comprises ten command execution modules for the general functions of image processing, data processing and conversion, static and dynamic display and management of external data and models.

4 Decision Issue (A) Formulation Environmental Data Protection Data I/O & Textual or Graphic I/O Problem Formulation & I/O Management (B) Master Controller (Command Analyzer and Message Sender) (C) Map Layer Display Output Layer Formulation Map Layer Input Map Layer Editing Cartographic Display of Data Data Input & Editing Data Query Internal Data Conversion External Data Conversion External Model Management (D) Database Key (G) Environmental & Geographic Database (E) Model Base Management (H) Environmental & Optimization Model Base (F) Inference Engine (I) Knowledge Base Management (K) Customized Data & Map Management Other GIS & Database Other Models (J) Problems Rules Figure 3: Architecture of the EDSS (adopted from Leung et al., 2000)

5 Components D and E are modules designed for database and model base management. Components G and H are environmental and geographical databases and environmental model base respectively. Components F, I, and J are respectively the inference engine, knowledge base manager, and knowledge (rule) base. Component K is made up of modules for the user to expand the EDSS functionalities in terms of database and model expansion. Included in components A and B of the present EDSS are simple mechanisms for problem formulation and processing and the input/output module. Processing more complicated problems, however, is handled by components I and J, which are located inside the EDSS but independent of the kernel of the system. This interconnection configuration allows the core systems to utilize the environmental knowledge base, inference engine, command controller to perform analysis and deduction. In turn, the EDSS can provide, automatically or semi-automatically, solutions and decisions to environmental problems with user participation. Furthermore, the EDSS can perform analysis on problem solutions. On the other hand, development of a relevant knowledge base can be separated from the main system, or the external expert system shell can be integrated with the main system to alleviate development cost and time. The kernel of the EDSS, through the input/output module and problem formulation and processing component of A, can dynamically send inference requests to components F, I and J. Conversely, it is possible to initiate data requests from component F to the kernel of the EDSS. These type of requests can enable the EDSS to directly communicate with the user through the problem formulation and processing component. Such a feature is extremely important when dealing with unstructured and semi-structured environmental problems. For decision making involving these types of problems, it is necessary that the user be involved in the decision making process so that subjective decision preferences could be brought into the decision-making process of the EDSS. On the other hand, it is also possible for component F to send control messages directly to the kernel and requests decision data support, or to send problem processing results to the master controller of component B, and then to directly execute the modules of component C. This connection, in essence, completes the process of data exchange and the output of control decision results between the problem processing system and the other systems of the EDSS. For component G, the user can tailor make additional modules to increase the image processing functionalities of component C, and to communicate directly with users. This guarantees the openess, expandibility, and development potential of the EDSS. 3.0 APPLICATIONS IN OPTIMAL WATER TREATMENT PLAN IN THE PEARL RIVER DELTA In recent years, rapid economic development has led to the deterioration of the environment in the Pearl River Delta. The situation is especially acute in its tidal river network. The EDSS developed in section II has been employed to solve successfully some major environmental problems in the delta. They include the provision of basic data for the cross-boundary problems, sewage discharge, and location of water treatment facilities. In modeling the tidal river network, a total of 160 rivers were used after generalization. The total length is 1600 kilometers. The whole network is divided into 1207 control areas for calibration. The underlying tidal-river-network water quality model is made up of two components: the tidal flow process model and the water quality model (Leung et al., 1998a). The tidal flow model produces the spatio- temporal tidal flow distribution under the designated boundary conditions. The water quality model is based on the output of the tidal flow model. The water quality model has six major functions: 1. To predict seasonal tidal flow conditions at various river sections, such as the times of high and low tide; 2. To analyze the interplay of channel flow and tidal movement; 3. To analyze and predict the influence of down-stream water quality by channel construction water diversion works; 4. To calibrate channel flow in dry seasons; 5. To analyze the impact on water quality from major pollution sources; and 6. To determine, through trial and error, the allowable discharge rates for the city or the disposal site. For river networks with a one-dimensional flow pattern, the governing equations for tidal flow and water quality are given as follows: A t + Q x = 0 (1)

6 ( uq) Q + t x 2 Q ξ gn Q Q α + ga + = 0 (2) x x x AR 4/3 T t ( ut ) + x T E x x x + K( T Ts) = S LB (3) Where x = distance along the axis of a channel, meters; t = time, second(s); u = velocity along the axis of channel, m/s; ξ = water surface elevation, m; Q = flow rate, m 3 /s; A = cross-sectional area, m 2 ; g = acceleration of gravity, m/s 2 ; R = hydraulic radius, m; n = Manning roughness coefficient, s/m 1/3 ; α = eddy viscosity, m 2 /s; K = transformation rate constant, 1/s; T = concentration of water quality constituent, mg/l; T s = balance concentration, mg/l; E x = longitudinal mixing coefficient, m 2 /s; and S LB = loading rate. G/s. The discretization method made in Pantankar (1984) and Zeng et al. (1991) is used to solve the system of equations over the GIS-managed tidal river network. Facilities of the EDSS depicted in Fig. 3 are used to handle system I/O, model-database (GIS, DBMS) communication and other relevant operations. The EDSS user provides the time process, spatial distribution, temporal-spatial change, and result consultation in order to obtain the calculation result in maps and graphs and to entertain queries. The time process manipulation gives the user the information on how a certain physical quantity changes through time. The spatial distribution manipulation gives the user the information of a physical quantity in a two-dimensional space at a certain time. The spatio-temporal transformation manipulation gives the user the information for both time and space of a physical quantity. Based on the tidal-river-network water quality model, the optimal planning model for tidal-river-network waste water treatment is constructed to determine the waste water treatment facilities such that the total cost is minimized and the water quality standards of specified cross sections of the river are satisfied (Leung et al., 1998b). Upon assigning values to the parameters for the quantity of waste water and concentration level, the control profile water quality standards, waste water discharge standards and investment cost function, the model generates the optimal solution for the waste water treatment project. More specially, the solution includes the number and geographical location of waste water treatment plants; the capacity for every plant; the disposal rate; investment estimation; and the path, length, diameter, and cost estimation of waste water collection pipes. The waste-water-control optimal planning model of the tidal river network can be formulated as follows: N M inz = f Q i i Q ij i = 1 1 η i = 1j = 1 2 (4) N N (, ) + f ( ) s.t. AT B, T 0, Q i 0, Qij 0 (5) where Z is the total cost for the region s waste water control; N is the number of sites where waste water treatment plants can be established; Q i and η i are the ith feasible treatment site s waste water volume and disposal rate, respectively; Q ij is the amount of waste water at j being shipped to i; f 1 (Q, η) is the cost function of the waste water treatment plant; f 2 (Q) is the waste water transportation cost function; A is the matrix of control coefficients; T is the vector of waste loading discharge; and B is a vector of the allowable discharge quantities. The above model is a multivariate optimization planning model. The objective function of the optimization depends on two interdependent planning variables: Q and η. Heuristic solution method proposed by Hu and Xu

7 (1990) and a number of numerical examples appeared to show that it is possible to find the optimal solution of the problem. The model solution process begins with the division of the objective function in (4) into two parts: the waste water treatment plant cost, Z 1, and waste water transportation cost, Z 2. They are described in the following three equations: N = i= 1 ( ) Z1 f,η (6a) N N i= 1 j= 1 1 Q i i ( Q ) Z 2 = f 2 ij (6b) Z = Z 1 + Z 2 (6c) Then, by determining the waste water planning model for every individual feasible treatment site, the original problem can be divided into two problems: optimization of waste water disposal at every site and optimization of waste water transportation. The two optimizations can proceed independently and the results together form the first optimization search findings. Then, the scale of operation at all possible sites are readjusted, and the second optimization search begins. The findings from the second search are compared and contrasted with that from the initial search. This process continues until values of the objective function can no longer be improved. There are three types of constraints in the optimal planning model. The first type is on the allowable discharge rates, and it is imposed to prevent the discharged pollutant loading at every treatment site from exceeding the prescribed loading levels in the model. The second type is on discharge standards, and is designed to prevent the discharged pollutant concentration to exceed standards set by the national or local government. The third type includes the extra constraints that come about as a result of the linear nature of the cost function. Again facilities in the EDSS depicted in Fig. 3 are employed to handle model-database communications, user interface, expert opinions, and etc. The EDSS provides the user the planning result manipulation in order to achieve the optimal results. The outputs include the number of necessary waste water treatment plants, the treatment capacity of each plant, disposal rate and initial investment, waste water transportation pipes required, pipe diameter, length, direction, and costs. Furthermore, the EDSS allows the user to query the optimization results. Real-life applications have been made in various places of the delta. A typical example is the planning in Jiangmen region (Leung et al., 1998b). 4. CONCLUSION As information becomes more overwhelming and our decision making problems become more perplexing and complex, extensive utilization of human intuition, value judgement and knowledge in decision support systems will be ever increasing. Our ability to bring together a broad range of knowledge and data to support decision making will make the EDSS more intelligent. Such a system will evolve with changes in our environmental system and decision making environment. It will eventually become an extension of our faculties for solving spatio-temporal environmental problems in a cooperative and mutually enriching way. To reach such a symbiosis, we have to solve individually and integratively problems of information, knowledge, and human-machine interaction. The EDSS approach discussed in this paper attempts to advance such a synergy. With captured place and domain-specific knowledge, the EDSS can be used to solve a variety of water-related environmental problems in various places of the world. ACKNOWLEDGEMENTS This project was supported by the research grant CF92/10/45 of The Croucher Foundation, and grant

8 CUHK4037/97H of Hong Kong Research Grants Council. REFERENCES Hu, K.P. and Z.C. Xu. Study on the Optimization Model for Water Pollution Control in Tidal River Networks. Technical report, South China Institute of Environmental Sciences, 1990 (in Chinese). Leung, Y. Intelligent Spatial Decision Support Systems. Berlin: Springer-Verlag, Leung, Y. Towards the Development of an Intelligent Spatial Decision Support System, in M.M. Fischer and P. Nijkamp (eds.), Geographic Information Systems, Spatial Modelling, and Policy Evaluation, Berlin: Springer-Verlag, 1993, pp Leung, Y., Y. Lee, K.S. Leung, K.C. Lam, K. Lin and F.T. Zeng. An Environmental Decision Support System for the Management of Water Pollution in Tidal River Network: 1, Concepts and System Architecture, 2000 (unpublished paper) Leung, Y., Y. Lee, K.S. Leung, K. Lin and F.T. Zeng An Environmental Decision Support System for Tidal Flow and Water Quality Analysis in the Pearl River Delta, in Proceedings of International Conference on Modeling Geographical and Environmental Systems with Geographical Information Systems, 1998a, pp Leung, Y., Y. Lee, K.S. Leung, K. Lin and F.T. Zeng An Environmental Decision Support System for the Optimal Planning of Waste Water Treatment Facilities in the Pearl River Delta, in Proceedings of International Conference on Modeling Geographical and Environmental Systems with Geographical Information Systems, 1998b, pp Pantankar, S.V. A Numerical Method for Flows and Heat Transfer. Hefei, China: Anhui Science & Technology Publishers, Zeng, F.T., Z.C. Xu and X.C. Chen. A Real-time Mathematical Model for Tidal River Networks and Its Application to the Pearl River Delta, in Computational Methods in Water Resources, Vol. 2, Computational Mechanics Publications, U.K., pp